Benchmarking Cancer Imaging Foundation Models: A Comprehensive Guide for Researchers and Clinicians

Skylar Hayes Dec 02, 2025 7

This article provides a systematic review of the current landscape, methodologies, and challenges in benchmarking foundation models for cancer imaging.

Benchmarking Cancer Imaging Foundation Models: A Comprehensive Guide for Researchers and Clinicians

Abstract

This article provides a systematic review of the current landscape, methodologies, and challenges in benchmarking foundation models for cancer imaging. Aimed at researchers, scientists, and drug development professionals, it synthesizes recent evidence on the performance, optimization, and real-world validation of these AI models across diverse clinical tasks, including diagnosis, prognosis, and biomarker discovery. The content explores foundational concepts, practical implementation strategies, solutions for common pitfalls like data scarcity, and rigorous comparative validation frameworks. By consolidating insights from cutting-edge studies, this guide aims to inform the development of robust, generalizable, and clinically impactful AI tools for precision oncology.

The Rise of Foundation Models in Cancer Imaging: Definitions, Core Concepts, and Clinical Promise

Defining Foundation Models and Self-Supervised Learning in a Medical Context

Foundation Models (FMs) and Self-Supervised Learning (SSL) represent a transformative shift in how artificial intelligence is developed for medical imaging, particularly in oncology. These technologies address a fundamental limitation in traditional supervised deep learning: the dependency on vast, expensively annotated datasets. In the context of cancer imaging, where expert annotations are scarce, time-consuming, and often restricted by privacy concerns, FMs trained using SSL offer a promising alternative by learning from large volumes of unlabeled data [1] [2].

A Foundation Model is defined as "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks" [1]. In medical imaging, these models are pre-trained on extensive, diverse datasets and can subsequently be fine-tuned for specific clinical applications such as tumor segmentation, classification, or detection with minimal task-specific labeling [3] [1]. Self-Supervised Learning is the cornerstone methodology enabling this approach, where models learn meaningful representations from unlabeled data by solving "pretext" tasks—such as predicting missing image regions, reconstructing corrupted inputs, or identifying transformed versions of the same image [4] [5]. This paradigm reduces the annotation bottleneck while creating models that capture universal features transferable across various clinical tasks and imaging modalities.

Core Technical Concepts and Methodologies

The Three Pillars of Foundation Models

Foundation models in medical imaging are characterized by three core technical concepts that distinguish them from traditional deep learning approaches [1]:

  • Large-Scale Pre-Training: FMs leverage very large datasets, often comprising hundreds of thousands of unlabeled medical images from multiple institutions, modalities, and anatomical regions. This scale and diversity enable the model to learn robust, generalizable representations less affected by the biases and limitations of smaller, single-source datasets [1] [6]. For instance, the 3DINO-ViT model was pre-trained on approximately 100,000 3D scans from over 10 different organs [6].

  • Self-Supervised Learning Objectives: Instead of relying on manual labels, SSL uses the intrinsic structure of the data itself to create supervisory signals. Common pretext tasks in medical imaging include:

    • Contrastive Learning: Maximizing agreement between differently augmented views of the same image while minimizing similarity with other images [4].
    • Masked Image Modeling: Randomly masking portions of an image and training the model to reconstruct the missing parts [2].
    • Image-Text Alignment (for multimodal FMs): Learning associations between medical images and their corresponding radiology reports [2].

  • Efficient Downstream Adaptation: Once pre-trained, the foundation model's rich, general-purpose representations can be efficiently adapted to specific clinical tasks (e.g., breast lesion segmentation, lung nodule classification) through techniques like fine-tuning, linear probing, or prompt-based learning, often with very limited task-specific labeled data [1].

Architectural Enablers: Transformers and Beyond

The rise of FMs has been facilitated by advances in model architectures, particularly the Vision Transformer (ViT). Unlike Convolutional Neural Networks (CNNs) that process images with local filters, ViTs split an image into patches and use self-attention mechanisms to capture long-range dependencies and global contextual information [7] [4] [1]. This is especially valuable in medical imaging, where pathological features can be diffuse and context-dependent. Hybrid architectures that combine the local feature sensitivity of CNNs with the global contextual reasoning of transformers have also demonstrated synergistic performance for cancer imaging tasks [4].

Table 1: Key Architectural Components of Medical Imaging Foundation Models

Component Description Role in Medical FM Pipeline
Vision Transformer (ViT) Uses self-attention to model global dependencies between image patches [4]. Core backbone network for large-scale pre-training; excels at capturing context.
Convolutional Neural Network (CNN) Uses local, shared-weight filters to extract hierarchical features [1]. Often used in hybrid models or for specific downstream tasks requiring localized feature detection.
Self-Distillation A student network is trained to match the output of a teacher network on different augmented views of the same image [6]. SSL method used in frameworks like DINO/3DINO to learn robust features without labels.
Adapter Modules Lightweight, add-on networks that allow for fine-tuning of pre-trained models with minimal parameters [6]. Enables efficient adaptation of large FMs to new tasks or modalities without full retraining.

Comparative Performance in Cancer Imaging Benchmarks

Benchmarking SSL and Transformer-based Models

Empirical evidence from recent studies demonstrates the superior performance of SSL-pre-trained FMs compared to traditional supervised models, especially in data-scarce regimes common in medical applications. The following table summarizes key benchmarking results across various cancer imaging tasks.

Table 2: Performance Comparison of Foundation Models on Key Benchmarking Tasks

Model / Framework Pre-training Data Downstream Task Key Comparative Result Citation
3DINO-ViT ~100,000 3D scans (MRI, CT), multi-organ [6]. Brain Tumor (BraTS) Segmentation (Dice) 0.90 (with 10% labels) vs. 0.87 for supervised model from scratch [6].
3DINO-ViT ~100,000 3D scans (MRI, CT), multi-organ [6]. Abdominal Organ (BTCV) Segmentation (Dice) 0.77 (with 25% labels) vs. 0.59 for supervised model from scratch [6].
3DINO-ViT ~100,000 3D scans (MRI, CT), multi-organ [6]. COVID-19 Classification (AUC) Achieved an 18.9% higher AUC on average versus next best baseline [6].
SSL & Transformer Models Various unlabeled breast imaging datasets [7] [4]. Breast Lesion Classification SSL offers a "label-efficient strategy" achieving "strong performance" comparable to supervised models with a fraction of the labels [7].
ResNet-101 (Baseline) Mammo-Bench (19,731 images) [8]. Breast Cancer Classification (Accuracy) 78.8% accuracy on large, diverse benchmark vs. 25-69% on smaller, single-source datasets [8].
Detailed Experimental Protocol: The 3DINO Case Study

The 3DINO (3D self-distillation with no labels) framework provides a clear example of a rigorous benchmarking protocol for a medical imaging FM [6]. The methodology can be summarized as follows:

  • Pre-training Dataset Curation: An ultra-large, multimodal dataset of nearly 100,000 unlabeled 3D medical volumes was assembled from 35 public and internal sources. This included MRI (N=70,434), CT (N=27,815), and a small set of PET (N=566) scans from over 10 organs [6].

  • Self-Supervised Pre-training:

    • Framework: Adaptation of DINOv2 for 3D medical images.
    • Pretext Task: Self-distillation with an image-level and a patch-level objective. For each 3D volume, two global and eight local crops were generated via augmentations.
    • Objective: A student network is trained to match the output of a teacher network for both the global and local views of the same scan, encouraging the model to learn representations that are invariant to perturbations and salient at multiple scales [6].

  • Downstream Task Evaluation:

    • Tasks: Segmentation (Brain Tumors/BraTS, Abdominal Organs/BTCV) and Classification (Brain Age, COVID-19 vs. Pneumonia).
    • Benchmarking: 3DINO-ViT was compared against multiple baselines: models trained from scratch ("Random"), supervised transfer learning ("Swin Transfer"), and other SSL methods ("MIM-ViT").
    • Data Efficiency Test: Models were evaluated after fine-tuning on progressively smaller subsets (e.g., 10%, 25%, 50%, 100%) of the labeled downstream task data [6].

The workflow for this foundational pre-training and benchmarking process is illustrated below.

G cluster_pretrain Pre-training Phase (Self-Supervised) cluster_transfer Adaptation & Benchmarking A Large Unlabeled Dataset ~100k 3D Scans (MRI, CT) B Pretext Task: Self-Distillation A->B C Foundation Model (3DINO-ViT) Learned General Representations B->C D Fine-tuning on Labeled Data (e.g., 10%, 25%, 50%, 100%) C->D E Benchmark Performance (Segmentation Dice, Classification AUC) D->E F Comparison vs. Baselines (Supervised, Other SSL Methods) E->F

Foundation Model Pre-training and Benchmarking Workflow

The Scientist's Toolkit: Essential Research Reagents

Developing and benchmarking foundation models for cancer imaging requires a suite of data, computational tools, and methodological frameworks. The table below details key "research reagents" essential for work in this field.

Table 3: Essential Research Reagents for Cancer Imaging Foundation Model Research

Resource Type Example(s) Function and Utility
Large-Scale Benchmark Datasets Mammo-Bench (19,731 mammography images) [8], HNC PET/CT Dataset (1,123 studies) [9], EMB Melanoma Dataset [10]. Provides standardized, multi-institutional data for training and fair evaluation; critical for assessing model generalizability.
Pre-trained Model Weights 3DINO-ViT weights [6], MedSAM [3]. Enables researchers to skip costly pre-training and immediately fine-tune models on downstream tasks, accelerating research.
SSL Algorithms & Frameworks DINOv2 (adapted to 3DINO for medical data) [6], Masked Autoencoders (MAE) [2], Contrastive Learning (SimCLR, MoCo) [4]. Provides the core algorithmic machinery for learning from unlabeled data.
Federated Learning Platforms Mentioned as a key privacy-preserving method [3]. Enables multi-institutional collaboration on model training without sharing sensitive patient data, helping to build larger, more diverse datasets.
Visualization Tools Grad-CAM, Attention Maps [3] [4]. Provides visual explanations of model predictions, which is crucial for building clinician trust and understanding model failures.

The benchmarking data clearly indicates that foundation models and self-supervised learning are establishing a new state-of-the-art in cancer imaging AI. Their primary advantage lies in significantly improved data efficiency and generalizability, as demonstrated by strong performance when fine-tuned with minimal labeled data and on multi-centric benchmarks [6] [8].

The future trajectory of this field points toward several key priorities [7] [3] [2]. First, the development of larger, more diverse multimodal datasets that integrate imaging with pathology, genomics, and clinical outcomes will be essential. Second, creating more interpretable and transparent models is a critical step for fostering clinical trust and facilitating adoption. Finally, establishing decentralized validation frameworks for the continuous monitoring and updating of models in real-world clinical settings will be necessary to ensure their long-term safety and efficacy. As these models evolve from single-task tools to multi-dimensional intelligent analysis platforms, they hold the potential to fundamentally redefine precision oncology.

The field of artificial intelligence in medical imaging is undergoing a fundamental transformation, moving away from single-task models toward versatile foundation models. In computational pathology and radiology, traditional AI systems were typically designed and trained for narrow, specific tasks—such as detecting a particular cancer type or predicting a single biomarker. These specialist models, while often highly accurate within their limited domain, require extensive labeled data for each new task and lack the flexibility needed for comprehensive clinical deployment [11].

Cancer foundation models represent a paradigm shift. These are large-scale models pre-trained on vast, diverse datasets using self-supervised learning techniques, enabling them to learn generalizable representations from unlabeled data [12]. Once pre-trained, they can be adapted to numerous downstream tasks—from cancer detection and molecular profiling to survival prediction—with minimal task-specific fine-tuning [13] [14]. This emerging capability mirrors the revolution seen in natural language processing with models like ChatGPT, but tailored for the complex domain of medical imaging [13]. This guide provides a systematic comparison of these approaches through the lens of recent benchmarking studies, offering researchers objective performance data and methodological insights to inform model selection.

Performance Benchmarking: Generalist vs. Specialist Models

Large-Scale Evaluation in Computational Pathology

A comprehensive benchmarking study evaluated 19 histopathology foundation models on 31 clinically relevant tasks across 6,818 patients from lung, colorectal, gastric, and breast cancers [12]. The models were assessed on tasks spanning three domains: morphological classification, biomarker prediction, and prognostic outcome forecasting.

Table 1: Performance Comparison of Leading Pathology Foundation Models (Mean AUROC)

Model Model Type Morphology (5 tasks) Biomarkers (19 tasks) Prognosis (7 tasks) Overall (31 tasks)
CONCH Vision-Language 0.77 0.73 0.63 0.71
Virchow2 Vision-Only 0.76 0.73 0.61 0.71
Prov-GigaPath Vision-Only 0.74 0.72 0.60 0.69
DinoSSLPath Vision-Only 0.76 0.69 0.60 0.69
UNI Vision-Only 0.75 0.69 0.59 0.68

The data reveals that the top-performing vision-language model (CONCH) performed on par with the best vision-only model (Virchow2), achieving identical overall AUROC scores despite differences in their training approaches and data volumes [12]. CONCH was trained on 1.17 million image-caption pairs, while Virchow2 was trained on a substantially larger set of 3.1 million whole-slide images, suggesting that data diversity and training methodology may outweigh sheer data volume [12].

A key finding was that different foundation models trained on distinct datasets learn complementary features. When the predictions of CONCH and Virchow2 were combined in an ensemble, they outperformed individual models in 55% of tasks, demonstrating the potential of hybrid approaches that leverage the strengths of multiple architectures [12].

Real-World Validation for Endometrial Cancer Subtyping

A recent study provided a direct comparison between generalist foundation models and specialist convolutional neural networks (CNNs) for molecular subtyping of endometrial cancer from whole-slide images [15]. The research employed four ImageNet-pretrained CNNs (EfficientNet-B7, ResNet-18, ResNet-50, DenseNet-121) as specialists versus six foundation models (Virchow2, UNI2, CTransPath, Prov-GigaPath, H-Optimus-0) as generalists, using two multiple instance learning aggregators (CLAM and TransMIL).

Table 2: External Validation Performance for Endometrial Cancer Subtyping

Model Type Specific Model Macro-AUC (Internal) Macro-AUC (External) Performance Drop
Specialist CNN EfficientNet-B7 0.829 0.602 -0.227
Specialist CNN ResNet-50 0.815 0.589 -0.226
Generalist FM UNI2 + CLAM 0.847 0.780 -0.067
Generalist FM Virchow2 + CLAM 0.860 0.773 -0.087

The results demonstrated that foundation models consistently outperformed specialist CNNs in internal validation. More importantly, when tested on an independent external cohort of 720 patients, foundation models showed significantly better generalization with substantially smaller performance degradation [15]. The best-performing foundation model configuration (UNI2 with CLAM aggregator) maintained a macro-AUC of 0.780 on external validation, compared to 0.602 for the best CNN, highlighting the superior robustness of generalist models in real-world settings [15].

Multi-Cancer Evaluation of a Generalist Platform

The CHIEF (Clinical Histopathology Imaging Evaluation Foundation) model represents a comprehensive generalist approach designed to perform multiple diagnostic tasks across diverse cancer types [13]. Trained on 60,000 whole-slide images spanning 19 cancer types, CHIEF was evaluated on 32 independent datasets from 24 hospitals worldwide.

Table 3: CHIEF Generalist Model Performance Across Multiple Tasks

Task Category Specific Task Performance Comparison to Specialists
Cancer Detection 11 cancer types from biopsy 96% accuracy Outperformed specialists by up to 36%
Cancer Detection 5 cancer types from surgical resection >90% accuracy Consistent high performance
Genomic Prediction 54 cancer genes >70% accuracy Superior to specialist AI
Survival Prediction Multiple cancer types 8-10% improvement Outperformed other models

CHIEF demonstrated remarkable versatility, achieving high accuracy across cancer detection, genomic prediction, and survival forecasting without task-specific architecture modifications [13]. The model successfully identified mutations linked to targeted therapy response across 18 genes spanning 15 anatomic sites, achieving 96% accuracy for EZH2 mutation in lymphoma, 89% for BRAF in thyroid cancer, and 91% for NTRK1 in head and neck cancers [13]. This performance across multiple domains with a single architecture underscores the potential of generalist platforms to streamline AI integration into clinical workflows.

Experimental Protocols and Methodologies

Benchmarking Framework for Pathology Foundation Models

The large-scale benchmarking study employed a standardized evaluation protocol to ensure fair comparison across the 19 foundation models [12]. Whole-slide images were first tessellated into small, non-overlapping patches, after which feature extraction was performed using each foundation model. These extracted features then served as inputs for training multiple instance learning models tailored for specific tasks.

Experimental Workflow Diagram:

G cluster_0 Foundation Models WSI WSI Tessellation Tessellation WSI->Tessellation FeatureExtraction FeatureExtraction Tessellation->FeatureExtraction MILTraining MILTraining FeatureExtraction->MILTraining FM1 CONCH FM2 Virchow2 FM3 Prov-GigaPath FM4 UNI Prediction Prediction MILTraining->Prediction

The evaluation encompassed 31 clinically relevant tasks related to morphology (5 tasks), biomarkers (19 tasks), and prognostication (7 tasks) [12]. Models were assessed using area under the receiver operating characteristic curve (AUROC) as the primary metric, with complementary evaluation using area under the precision-recall curve (AUPRC), balanced accuracy, and F1 scores. To mitigate data leakage concerns—a critical issue in foundation model evaluation—the study utilized multiple proprietary cohorts from different countries that were never part of any foundation model training sets [12].

STAMP Pipeline for Endometrial Cancer Subtyping

The endometrial cancer subtyping study employed the STAMP (Solid-Tumour Associative Modelling in Pathology) framework, an open-source pipeline that combines tiling, feature extraction using foundation models, and multiple instance learning-based prediction [15]. The methodology involved several key stages:

  • Cohort Assembly: A public discovery cohort of 815 patients (1,195 WSIs) from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium was assembled for model development, with an independent external cohort of 720 patients (1,357 WSIs) reserved for validation [15].

  • Preprocessing: Whole-slide images were tessellated into 256×256 micron tissue patches, followed by quality filtering to exclude background patches and out-of-focus regions using canny edge masks [15].

  • Feature Extraction: Tile embeddings were generated using frozen foundation model encoders, with weights kept constant during training [15].

  • Multiple Instance Learning: Embeddings were aggregated into slide-level predictions using either TransMIL (transformer-based) or CLAM (attention-based with instance-level supervision) architectures [15].

  • Evaluation: Models were assessed using five-fold cross-validation with macro-AUC (averaging per-subtype AUCs) as the primary outcome metric, complemented by macro-F1 score and balanced accuracy to account for class imbalance [15].

Robustness Assessment in Low-Data Scenarios

A critical consideration for clinical deployment is model performance in data-scarce settings, particularly for rare molecular events. The benchmarking study specifically evaluated foundation models across varying data regimes by training downstream models on randomly sampled cohorts of 300, 150, and 75 patients while maintaining similar positive sample ratios [12].

The results revealed interesting dynamics: while Virchow2 demonstrated superior performance in the largest sampled cohort (n=300), PRISM dominated in medium-sized cohorts (n=150), and the smallest cohort (n=75) showed more balanced results with CONCH leading in the most tasks [12]. This suggests that while generalist foundation models overall reduce data requirements, their relative strengths may vary depending on specific data availability scenarios.

The Scientist's Toolkit: Essential Research Reagents

Table 4: Key Experimental Resources for Foundation Model Research

Resource Category Specific Resource Function in Research Example Implementations
Feature Extractors CONCH, Virchow2, UNI, Prov-GigaPath, CTransPath Generate tile-level embeddings from whole-slide images CONCH: Vision-language model trained on 1.17M image-caption pairs [12]
MIL Aggregators CLAM, TransMIL Combine tile embeddings into slide-level predictions CLAM: Uses attention pooling with instance-level supervision [15]
Benchmarking Frameworks STAMP, TumorImagingBench Standardized evaluation pipelines for fair model comparison STAMP: Open-source pipeline for solid tumour modelling [15]
Evaluation Metrics AUROC, AUPRC, Balanced Accuracy, F1 Score Quantify model performance across classification tasks Macro-AUC: Averages per-subtype AUCs for class-imbalanced data [15]
Public Datasets TCGA, CPTAC, Independent Cohorts Provide diverse, clinically relevant data for training and validation TCGA-UCEC: 523 endometrial cancer patients with molecular data [15]

The comprehensive benchmarking evidence presented demonstrates that generalist foundation models represent a substantial advancement over traditional single-task AI systems in cancer imaging. While specialist models continue to have value in specific, narrow applications, generalist models like CONCH, Virchow2, and CHIEF offer superior versatility, robustness to real-world distribution shifts, and reduced data requirements for new tasks [12] [15] [13].

The paradigm shift toward generalist models is particularly significant for clinical translation, as it addresses key bottlenecks in the development and deployment of AI tools for cancer diagnosis and treatment guidance. Foundation models enable more efficient resource utilization by providing a single, powerful feature extractor that can be fine-tuned for multiple clinical tasks without requiring complete retraining [12]. Moreover, their demonstrated ability to maintain performance across diverse patient cohorts and imaging protocols suggests greater potential for widespread clinical adoption [15] [13].

As the field continues to evolve, the integration of multi-modal data—combining histopathology with genomic, clinical, and radiological information—likely represents the next frontier in generalist medical AI. The models and methodologies detailed in this guide provide a foundation for researchers to build upon in developing the next generation of comprehensive cancer imaging tools.

Foundation models are revolutionizing computational pathology by providing powerful, general-purpose feature extractors that can be adapted to a wide range of clinical tasks. This guide objectively benchmarks the performance of leading histopathology foundation models across key clinical applications, from early cancer screening to prognostic biomarker discovery. Based on independent evaluation of 19 foundation models across 13 patient cohorts with 6,818 patients and 9,528 slides, we provide comparative performance data to help researchers and drug development professionals select optimal models for their specific cancer imaging tasks.

Performance Benchmarking of Pathology Foundation Models

Independent benchmarking of 19 foundation models reveals significant performance variations across different clinical domains. The evaluation encompassed 31 clinically relevant tasks categorized into morphological assessment (5 tasks), biomarker prediction (19 tasks), and prognostic outcome prediction (7 tasks) using external cohorts never part of any foundation model training to prevent data leakage [12].

Table 1: Overall Model Performance by Clinical Application Domain (Mean AUROC)

Foundation Model Morphology (n=5) Biomarkers (n=19) Prognosis (n=7) Overall Average
CONCH 0.77 0.73 0.63 0.71
Virchow2 0.76 0.73 0.61 0.71
Prov-GigaPath - 0.72 - 0.69
DinoSSLPath 0.76 - - 0.69
UNI - - - 0.68
BiomedCLIP - - 0.61 0.66

Performance on Specific Cancer Types

Model performance varies across cancer types, with different models excelling in different tissue contexts. This specialization reflects variations in training data composition and model architecture [12].

Table 2: Top-Performing Models by Cancer Type (AUROC)

Cancer Type Best Performing Model Key Strength Area
STAD (Stomach Adenocarcinoma) CONCH Morphological assessment
NSCLC (Non-Small Cell Lung Cancer) CONCH Biomarker prediction
CRC (Colorectal Cancer) Virchow2 Prognostic outcome
BRCA (Breast Cancer) BiomedCLIP Multi-task performance

Experimental Protocols and Methodologies

Benchmarking Framework Design

The comprehensive benchmarking study employed a standardized evaluation framework to ensure fair comparison across models [12]:

  • Feature Extraction: Whole-slide images (WSIs) were tessellated into small, non-overlapping patches, after which image feature extraction was performed using each foundation model.

  • Model Training: The extracted features served as inputs for training transformer-based classification models tailored for specific clinical tasks.

  • Task Categories:

    • Morphological Tasks: Tissue composition and structural characteristics
    • Biomarker Tasks: Prediction of 19 different cancer biomarkers
    • Prognostic Tasks: 7 different survival and outcome predictions

  • Evaluation Metrics: Primary evaluation used Area Under the Receiver Operating Characteristic Curve (AUROC), with additional validation using Area Under the Precision-Recall Curve (AUPRC), balanced accuracy, and F1 scores.

Weakly-Supervised Learning Approach

The benchmarking utilized weakly-supervised multiple instance learning (MIL) frameworks, which are particularly valuable in computational pathology where slide-level labels are more readily available than patch-level annotations [12]. The transformer-based aggregation approach slightly outperformed traditional attention-based MIL (ABMIL), with an average AUROC difference of 0.01.

G A Input WSIs B Tessellation into Patches A->B C Feature Extraction (Foundation Model) B->C D Weakly-Supervised Training (Multiple Instance Learning) C->D E Task-Specific Predictions (Biomarkers, Prognosis, Morphology) D->E

Performance in Low-Data Scenarios

A critical evaluation dimension assessed model performance in data-scarce settings that reflect real-world clinical scenarios with rare molecular events. Downstream models were trained on randomly sampled cohorts of 300, 150, and 75 patients while maintaining similar positive sample ratios [12]:

  • Large cohorts (n=300): Virchow2 demonstrated superior performance in 8 tasks
  • Medium cohorts (n=150): PRISM dominated, leading in 9 tasks
  • Small cohorts (n=75): CONCH led in 5 tasks, with balanced results across models

Clinical Workflow Integration

From Whole-Slide Imaging to Clinical Predictions

Foundation models integrate into standardized computational pathology workflows to transform raw whole-slide images into clinically actionable predictions [12].

G A H&E Stained Tissue Section B Whole-Slide Digital Scanning A->B C Patch-Based Feature Extraction (CONCH, Virchow2, etc.) B->C D Clinical Task Prediction (Biomarkers, Prognosis, Morphology) C->D E Clinical Decision Support D->E

Biomarker Discovery and Validation Framework

Statistical Considerations for Biomarker Development

The journey from biomarker discovery to clinical application requires rigorous statistical validation [16]:

  • Biomarker Definition: A measurable indicator of normal biological processes, pathogenic processes, or responses to therapeutic interventions.

  • Validation Metrics:

    • Sensitivity: Proportion of true positives correctly identified
    • Specificity: Proportion of true negatives correctly identified
    • Discrimination: Ability to distinguish cases from controls, measured by AUROC
    • Calibration: How well predicted risks match observed risks

  • Statistical Power: Adequate sample size calculations with sufficient clinical events to detect meaningful effects.

Types of Biomarkers in Oncology

Foundation models can derive various biomarker classes with distinct clinical applications [16] [17]:

Table 3: Biomarker Types and Clinical Applications

Biomarker Type Clinical Role Foundation Model Application
Diagnostic Confirm disease presence Tissue classification and cancer subtyping
Prognostic Predict disease outcome Survival analysis and recurrence prediction
Predictive Forecast treatment response Therapy response biomarkers
Pharmacodynamic Measure drug effects Treatment effect monitoring

Research Reagent Solutions and Essential Materials

Successful implementation of foundation models in computational pathology requires specific research tools and resources [12] [18] [19].

Table 4: Essential Research Tools for Computational Pathology

Resource Category Specific Tools/Platforms Research Application
Foundation Models CONCH, Virchow2, Prov-GigaPath, UNI, DinoSSLPath Feature extraction from histopathology images
Annotation Tools Semi-supervised learning frameworks, Attention U-Net Reduced annotation burden for training
Computational Resources High-performance computing clusters, GPU acceleration Model training and inference
Data Resources TCGA, Mass-100K, Providence, MSKCC datasets Model pretraining and validation
Analysis Frameworks Multiple Instance Learning, Transformer aggregators Weakly-supervised learning for whole slides

Performance Optimization Strategies

Model Selection Guidelines

Based on comprehensive benchmarking, researchers can optimize performance through strategic model selection [12]:

  • For general-purpose applications: CONCH and Virchow2 provide the most consistent performance across diverse tasks.

  • For vision-language integration: CONCH demonstrates superior capability in leveraging multimodal data.

  • For data-scarce environments: Virchow2 maintains robust performance even with limited training samples.

  • For ensemble approaches: Combining CONCH and Virchow2 predictions outperforms individual models in 55% of tasks by leveraging complementary strengths.

Impact of Training Data Characteristics

Benchmarking reveals that data diversity outweighs data volume for foundation model performance [12]. While positive correlations exist between downstream performance and pretraining dataset size (r=0.29-0.74), the distribution of anatomic tissue sites, architecture, and dataset quality play more critical roles than sheer volume.

The field of computational oncology is undergoing a paradigm shift, moving from single-task diagnostic models toward multi-dimensional intelligent analysis powered by foundation models [3]. These models, pre-trained on massive datasets, learn universal representations that can be adapted to various downstream clinical tasks with minimal fine-tuning. As medical imaging foundation models proliferate, rigorous independent benchmarking becomes essential to guide researchers and clinicians in selecting optimal architectures for specific cancer imaging tasks. This evaluation is particularly crucial in clinical environments where diagnostic accuracy directly impacts patient outcomes, especially in high-stakes domains like emergency and critical care settings where model reliability is paramount [20].

Benchmarking studies help uncover important adjustments needed for future model improvements and reveal complementary strengths across different architectural approaches. A comprehensive understanding of how convolutional neural networks (CNNs), Vision Transformers (ViTs), and Vision-Language Models (VLMs) perform across diverse cancer imaging tasks—from biomarker prediction to morphological analysis and prognostication—enables more informed model selection and development [12]. This guide provides an objective comparison of these architectures' performance, supported by experimental data from recent benchmarking studies in computational pathology and oncology.

Fundamental Operational Differences

The operational mechanisms of CNNs, Transformers, and VLMs diverge significantly, impacting their performance characteristics across medical imaging tasks. CNNs operate through a series of hierarchical layers that detect features using localized filters, beginning with simple patterns and progressively combining them into more complex representations. This inductive bias toward local connectivity and translation invariance has made CNNs highly effective for many visual recognition tasks [21]. In contrast, Vision Transformers (ViTs) process images by dividing them into patches, converting these into token sequences, and applying self-attention mechanisms to model global relationships across the entire image from the earliest layers [21]. This global receptive field enables ViTs to capture long-range dependencies but typically requires larger datasets for effective training.

Vision-Language Models (VLMs) represent a more recent architectural innovation that combines visual understanding with language processing. These models typically employ a two-stage process: extracting visual features through a dedicated vision backbone (either CNN or Transformer-based) and textual features through a language module, then mapping these into a shared embedding space using cross-modal attention or contrastive learning [20]. This architecture enables VLMs to perform tasks requiring joint understanding of visual content and associated textual information, such as generating diagnostic reports from medical images.

ArchitectureComparison cluster_cnn CNN Architecture cluster_transformer Vision Transformer Architecture cluster_vlm Vision-Language Model Architecture CNN_Input Input Image CNN_Conv1 Convolutional Layers CNN_Input->CNN_Conv1 CNN_Pool1 Pooling Layers CNN_Conv1->CNN_Pool1 CNN_Conv2 Convolutional Layers CNN_Pool1->CNN_Conv2 CNN_Pool2 Pooling Layers CNN_Conv2->CNN_Pool2 CNN_Features Feature Maps CNN_Pool2->CNN_Features CNN_FC Fully Connected Layers CNN_Features->CNN_FC CNN_Output Classification Output CNN_FC->CNN_Output ViT_Input Input Image ViT_Patches Image Patches ViT_Input->ViT_Patches ViT_Embed Patch Embedding ViT_Patches->ViT_Embed ViT_Position Positional Encoding ViT_Embed->ViT_Position ViT_Transformer Transformer Encoder (Multi-Head Self-Attention) ViT_Position->ViT_Transformer ViT_MLP MLP Head ViT_Transformer->ViT_MLP ViT_Output Classification Output ViT_MLP->ViT_Output VLM_Image Medical Image VLM_Vision Vision Encoder (CNN or ViT) VLM_Image->VLM_Vision VLM_Text Clinical Text VLM_TextEnc Text Encoder (Transformer) VLM_Text->VLM_TextEnc VLM_Multimodal Multimodal Fusion (Cross-Attention) VLM_Vision->VLM_Multimodal VLM_TextEnc->VLM_Multimodal VLM_Joint Joint Representation VLM_Multimodal->VLM_Joint VLM_Output Diagnostic Output VLM_Joint->VLM_Output

Figure 1: Architectural comparison of CNNs, Vision Transformers, and Vision-Language Models, highlighting their distinct approaches to processing medical images.

Comparative Strengths and Limitations

Each architecture presents distinct advantages and limitations for cancer imaging applications. CNNs benefit from translation invariance and parameter efficiency due to weight sharing across spatial locations. Their hierarchical feature extraction aligns well with the compositional nature of medical images, where local patterns (e.g., cell structures) combine to form more complex tissues. Studies have demonstrated that well-optimized CNNs can achieve high performance in specific diagnostic tasks, with one investigation reporting accuracy between 89% and 98% for breast cancer classification using architectures like VGG16, ResNet, and EfficientNet [22]. However, CNNs' local receptive fields can limit their ability to capture long-range dependencies in histology whole-slide images or radiographic scans.

Vision Transformers excel at modeling global contextual relationships through self-attention mechanisms, potentially capturing diagnostically relevant patterns distributed across large tissue regions. Their large field of view from the initial layers enables better integration of spatially separated image regions [21]. Additionally, ViTs typically have a smaller memory footprint during training compared to CNNs, as they process image patches rather than maintaining extensive intermediate activation maps [21]. The trade-off involves typically higher computational requirements for self-attention operations and greater data hunger during pre-training.

Vision-Language Models offer unique capabilities for integrating imaging data with clinical context, enabling applications that require joint understanding of visual patterns and associated textual information (e.g., radiology reports, pathology notes, genomic data). This multimodal understanding is particularly valuable in precision oncology, where treatment decisions increasingly depend on synthesizing diverse data sources [3]. However, current VLMs face challenges in specialized medical domains, with one benchmarking study reporting that open-source VLMs achieved only up to 40.4% accuracy on diagnostic questions in emergency and critical care settings, significantly lagging behind proprietary models like GPT-4o (68.1%) [20].

Benchmarking Performance Across Cancer Imaging Tasks

Large-Scale Foundation Model Evaluation

A comprehensive benchmarking study evaluating 19 histopathology foundation models across 13 patient cohorts with 6,818 patients and 9,528 slides provides robust performance comparisons across lung, colorectal, gastric, and breast cancers [12]. The models were evaluated on 31 weakly supervised tasks related to biomarkers, morphological properties, and prognostic outcomes using area under the receiver operating characteristic curve (AUROC) as the primary metric.

Table 1: Performance of Top Foundation Models Across Different Task Categories in Computational Pathology

Model Architecture Type Morphology Tasks (Mean AUROC) Biomarker Tasks (Mean AUROC) Prognosis Tasks (Mean AUROC) Overall Mean AUROC
CONCH Vision-Language 0.77 0.73 0.63 0.71
Virchow2 Vision-Only 0.76 0.73 0.61 0.71
Prov-GigaPath Vision-Only 0.69 0.72 0.63 0.69
DinoSSLPath Vision-Only 0.76 0.68 0.60 0.69
UNI Vision-Only 0.74 0.68 0.59 0.68
BiomedCLIP Vision-Language 0.71 0.66 0.61 0.66

The benchmarking revealed that CONCH, a vision-language model trained on 1.17 million image-caption pairs, achieved the highest overall performance, matching Virchow2 (a vision-only model trained on 3.1 million whole-slide images) [12]. This demonstrates that VLM architectures can compete with and sometimes surpass specialized vision-only models, even when trained on fewer overall images. The superior performance of VLMs suggests that incorporating textual descriptions during pre-training may help models learn more clinically relevant representations.

Across different cancer types, the best-performing models varied: CONCH achieved the highest average AUROC in stomach adenocarcinoma (STAD) and non-small-cell lung cancer (NSCLC), Virchow2 led in colorectal cancer (CRC), and BiomedCLIP performed best in breast cancer (BRCA) [12]. This indicates that architectural advantages may be context-dependent and influenced by factors such as tissue type and specific clinical task.

Performance in Data-Scarce Settings

An important consideration in clinical applications is model performance when limited labeled data is available for fine-tuning. The benchmarking study evaluated this by training downstream models on randomly sampled cohorts of 300, 150, and 75 patients while maintaining similar ratios of positive samples [12].

Table 2: Model Performance Variations Across Different Data Availability Scenarios

Training Sample Size Best Performing Model Number of Tasks Where Model Led Key Observations
300 patients Virchow2 (Vision-Only) 8 tasks Vision-only models dominate in higher-data scenarios
150 patients PRISM (Vision-Only) 9 tasks Specialized architectures excel with moderate data
75 patients CONCH (VLM) 5 tasks VLMs show relative strength in low-data settings

In the largest sampled cohort (n=300), Virchow2 demonstrated superior performance in 8 tasks, while with the medium-sized cohort (n=150), PRISM dominated by leading in 9 tasks [12]. Interestingly, with the smallest cohort (n=75), CONCH led in 5 tasks, with PRISM and Virchow2 each leading in 4 tasks, suggesting that VLMs may offer advantages in very low-data scenarios despite their overall performance being less pronounced in low-prevalence tasks [12]. Performance metrics remained relatively stable between n=75 and n=150 cohorts, indicating that foundation models can maintain effectiveness even with substantial reductions in fine-tuning data.

CNN Performance in Specific Diagnostic Tasks

While foundation models represent the cutting edge, CNNs continue to demonstrate strong performance in well-defined classification tasks. In breast cancer detection, studies have shown that CNN architectures can achieve high accuracy, with ResNet reaching 97.4% accuracy and 0.98 AUC-ROC in classifying malignant versus benign tumors [22]. Similarly, for invasive ductal carcinoma (IDC) grading, comprehensive comparisons of seven CNN models found that EfficientNetV2B0-21k outperformed other architectures with a balanced accuracy of 0.9666 ± 0.0185, while all tested CNNs performed well with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set [23].

The performance of CNNs is also influenced by input image resolution. One study investigating breast ultrasound images found that optimal resolution varied by architecture: MobileNet performed best at 320×320 pixel resolution, while DenseNet121 achieved peak performance at 448×448 pixels [24]. This highlights the importance of matching architectural choices with appropriate data preprocessing strategies.

Experimental Protocols and Methodologies

Benchmarking Framework Design

Robust benchmarking of cancer imaging models requires carefully designed evaluation frameworks that mitigate potential data leakage and ensure clinically relevant assessments. The major benchmarking study discussed employed several key methodological safeguards [12]:

  • Multi-Cohort Validation: Models were evaluated on proprietary cohorts from multiple countries that were never part of any foundation model training, effectively mitigating the risk of data leakage from pretraining datasets.
  • Diverse Task Selection: The evaluation included 31 clinically relevant tasks—19 for biomarker prediction, 5 for morphology assessment, and 7 for prognostication—ensuring comprehensive assessment across different clinical needs.
  • Weakly Supervised Learning: The benchmark utilized multiple instance learning (MIL) approaches with transformer-based aggregation, which is particularly suitable for whole-slide images where slide-level labels are available but patch-level annotations are scarce.
  • Statistical Significance Testing: Performance differences were assessed for statistical significance using pairwise AUROC comparisons across 29 binary classification tasks, with P < 0.05 considered significant.

BenchmarkingWorkflow cluster_phase1 Phase 1: Model Selection & Preparation cluster_phase2 Phase 2: Multi-Cohort Data Curation cluster_phase3 Phase 3: Clinical Task Formulation cluster_phase4 Phase 4: Comprehensive Evaluation Step1 19 Foundation Models Selected Step2 Diverse Architectures (CNNs, ViTs, VLMs) Step1->Step2 Step3 Feature Extraction & Embedding Generation Step2->Step3 Step4 6,818 Patients 9,528 Slides Step3->Step4 Step5 4 Cancer Types (Lung, Colorectal, Gastric, Breast) Step4->Step5 Step6 External Cohorts (No Training Data Overlap) Step5->Step6 Step7 31 Clinical Tasks Step6->Step7 Step8 Biomarker Prediction (19 Tasks) Step7->Step8 Step9 Morphology Assessment (5 Tasks) Step8->Step9 Step10 Prognostication (7 Tasks) Step9->Step10 Step11 Multiple Instance Learning (Transformer Aggregator) Step10->Step11 Step12 Performance Metrics (AUROC, AUPRC, Balanced Accuracy) Step11->Step12 Step13 Statistical Significance Testing (Pairwise AUROC) Step12->Step13 Step14 Data-Scarce Scenario Analysis Step13->Step14

Figure 2: Comprehensive benchmarking workflow for evaluating cancer imaging foundation models, illustrating the multi-phase approach from model selection through comprehensive evaluation.

Evaluation Metrics and Statistical Analysis

Consistent evaluation metrics are essential for fair model comparisons. The primary metric used in comprehensive benchmarking studies is the Area Under the Receiver Operating Characteristic Curve (AUROC), which measures the model's ability to distinguish between classes across all classification thresholds [12]. Additional metrics provide complementary insights:

  • Area Under the Precision-Recall Curve (AUPRC): Particularly valuable for imbalanced datasets common in medical applications where positive cases may be rare.
  • Balanced Accuracy: Accounts for class imbalance by computing the average accuracy per class.
  • F1-Score: Harmonic mean of precision and recall, providing a single metric that balances both concerns.

Statistical significance testing is crucial when comparing model performance. The benchmarking study conducted pairwise AUROC comparisons across tasks, with significance determined using DeLong's test or similar methods [12]. A model was considered significantly better than another if it achieved higher AUROC with P < 0.05 in a substantial number of tasks.

Researchers evaluating architectural approaches for cancer imaging require access to curated datasets and computational frameworks. The following resources have been instrumental in recent benchmarking efforts:

Table 3: Essential Research Resources for Cancer Imaging Model Development

Resource Category Specific Examples Key Features & Applications
Histopathology Datasets TCGA (The Cancer Genome Atlas) Multi-modal data with genomic correlates for multiple cancer types [12]
Mass-100K (100,000 WSIs) Large-scale dataset for foundation model pre-training [12]
Providence (171,000 WSIs) Diverse whole-slide image collection for self-supervised learning [12]
Radiomics Benchmarks TumorImagingBench Curated benchmark with 3,244 scans and varied oncological endpoints [25]
Clinical VLM Evaluation DrVD-Bench Comprehensive benchmark for clinical visual reasoning with 7,789 image-question pairs [26]
NEJM Image Challenge Diagnostic questions with clinical images for acute care evaluation [20]
Computational Frameworks TensorFlow/PyTorch Deep learning frameworks with medical imaging extensions [23] [22]
Multiple Instance Learning (MIL) Weakly supervised approach for whole-slide image analysis [12]

Experimental Considerations for Robust Evaluation

When designing experiments to compare architectural approaches, researchers should incorporate several methodological safeguards:

  • Data Leakage Prevention: Ensure no overlap between pre-training and evaluation cohorts, using proprietary external datasets for validation when possible [12].
  • Clinical Relevance: Include tasks that reflect real-world clinical decision-making, such as biomarker prediction, morphological assessment, and prognostic estimation [12].
  • Multiple Performance Dimensions: Evaluate not only overall accuracy but also robustness in low-data settings, computational efficiency, and performance on rare positive cases [12] [20].
  • Ablation Studies: Systematically analyze the contribution of different architectural components to understand the sources of performance advantages.

Future Directions and Clinical Translation

The benchmarking results point toward several promising research directions. The complementary strengths of different architectures suggest potential benefits from strategic ensembling—the benchmarking study found that an ensemble combining CONCH and Virchow2 predictions outperformed individual models in 55% of tasks [12]. This indicates that fusion approaches leveraging both vision-language and vision-only models may yield superior performance for complex classification scenarios.

Future architectural innovations will likely focus on enhancing model efficiency and clinical applicability. Lightweight architectures like TinyViT and knowledge distillation techniques are being explored to reduce hardware requirements and facilitate broader clinical adoption [3]. Additionally, specialized training strategies that incorporate medical curriculum learning—exposing models to progressively more complex concepts similar to medical education—may enhance clinical reasoning capabilities [26].

For successful clinical translation, models must not only achieve high accuracy but also provide interpretability and seamless integration into clinical workflows. Visualization tools like Grad-CAM and vision-language frameworks such as Visual Question Answering (VQA) improve interpretability and foster collaboration between AI systems and clinicians [3]. As these technologies mature, their ability to integrate multimodal data streams, reduce diagnostic errors, and support time-sensitive workflows could transform cancer care delivery in diverse clinical settings.

Comprehensive benchmarking reveals that no single architecture universally dominates across all cancer imaging tasks. Vision-language models like CONCH demonstrate strong overall performance, particularly for tasks benefiting from integrated visual and textual understanding. Vision Transformers show advantages in capturing long-range dependencies in whole-slide images, while well-optimized CNNs remain competitive for specific classification tasks, especially in resource-constrained environments. The optimal architectural choice depends on multiple factors, including the specific clinical task, data availability, computational resources, and integration requirements. As the field advances, ensemble approaches and hybrid architectures that leverage the complementary strengths of multiple paradigms may offer the most promising path toward clinically impactful cancer imaging AI systems.

Implementing Foundation Models: From Data Preprocessing to Downstream Task Fine-Tuning

The effectiveness of Artificial Intelligence (AI) models in cancer imaging and computational pathology depends critically on the quality, standardization, and fairness of the input data [27]. Foundation models, which are large-scale, pre-trained AI models designed for adaptation to various downstream tasks, have emerged as powerful tools for extracting clinically relevant information from vast datasets [12] [28]. However, their performance and generalizability are not guaranteed. Recent comprehensive benchmarking studies reveal that model performance can vary significantly, and without rigorous validation on independent, multi-center cohorts, there is a risk of data leakage and selective reporting of only the best-performing models [12] [15]. This underscores a central thesis: the creation of high-quality, multi-center data repositories through meticulous data curation and preprocessing is not merely a preliminary step but the foundational pillar that determines the success and reliability of downstream foundation model applications in cancer research. This guide compares data curation methodologies and their impact on model performance, providing researchers with the experimental protocols and tools necessary for building robust data resources.

The Critical Role of Data Quality in Model Performance

The adage "garbage in, garbage out" is acutely relevant in AI-driven cancer research. Data derived from diverse sources—such as Electronic Health Records (EHRs), Picture Archiving and Communication Systems (PACS), and multi-omics analyses—are inherently heterogeneous and fragmented [27]. This variability compromises model performance and generalizability.

A benchmarking study of 19 histopathology foundation models across 13 patient cohorts and 31 clinical tasks demonstrated that data diversity in pretraining datasets can outweigh data volume [12]. For instance, the vision-language model CONCH, trained on 1.17 million image-caption pairs, outperformed or matched models trained on much larger datasets, achieving the highest overall average AUROC of 0.71 [12]. This highlights that a strategically curated, diverse dataset is more valuable than a merely large one.

Furthermore, the pre-validation of data is essential for identifying and mitigating issues that lead to biased and unfair AI models. The INCISIVE project's framework identified key problems such as missing clinical information, inconsistent formatting, and subgroup imbalances, which, if unaddressed, would have compromised the robustness of any subsequent AI service developed from the repository [27].

A Framework for Data Quality Assurance

To systematically address data challenges, a structured, multi-dimensional validation framework is essential. The following section outlines a proven methodology for pre-validating data prior to its use in AI development.

Core Dimensions of Data Quality

The INCISIVE project's framework assesses data across five key dimensions [27]:

  • Completeness: Verifies that all mandatory data fields are populated.
  • Validity: Ensures data conforms to the required syntax and format (e.g., date formats, value ranges).
  • Consistency: Checks that data does not contain contradictory information across different sources or time points.
  • Integrity & Uniqueness: Involves deduplication procedures to ensure each data record is unique and correctly linked.
  • Fairness: Evaluates the balanced representation of key demographic and clinical subgroups (e.g., sex, age, cancer grade) to prevent biased models.

Experimental Protocol for Data Pre-Validation

Applying this framework involves a concrete, step-by-step experimental protocol.

  • Hypothesis: Rigorous pre-validation of a multi-center cancer imaging repository will significantly improve the performance, robustness, and fairness of foundation models trained or evaluated on it.
  • Materials: The protocol requires a federated repository architecture, such as the one used in the INCISIVE project, comprising decentralized data storage units within data providers' premises and a central orchestrating platform [27]. Data includes imaging (e.g., CT, PET-CT, MRI, mammography stored as DICOM files) and matched clinical metadata for specific cancer types (e.g., lung, breast, colorectal, prostate) [27].
  • Procedure:
    • Data Collection & Federation: Aggregate imaging and clinical data from multiple healthcare systems into a federated repository, ensuring data remains local for privacy.
    • Structured Entry & Harmonization: Enforce standardized data entry protocols and common data models to ensure consistent structure and format.
    • Deduplication: Execute algorithms to identify and merge duplicate patient records.
    • Metadata Analysis: Analyze DICOM metadata for consistency and completeness.
    • Annotation Verification: Verify the quality and consistency of expert annotations.
    • Fairness & Subgroup Analysis: Actively assess the representation of demographic and clinical subgroups to identify imbalances.
    • Anonymization Check: Ensure compliance with privacy regulations and check for residual protected health information (PHI) in images and metadata.

The workflow for this protocol is detailed in the diagram below.

Start Multi-Center Data Sources A Data Collection & Federation Start->A B Structured Entry & Harmonization A->B C Deduplication B->C D Metadata Analysis B->D E Annotation Verification B->E F Fairness & Subgroup Analysis B->F G Anonymization Check B->G End High-Quality Federated Repository C->End D->End E->End F->End G->End

Comparative Performance of Foundation Models

The ultimate test of data quality is the performance of foundation models on curated repositories. Independent benchmarking on well-prepared, multi-center cohorts provides critical insights for model selection.

Benchmarking on Histopathology Tasks

A large-scale study evaluated 19 foundation models on 9,528 slides from 6,818 patients across lung, colorectal, gastric, and breast cancers [12]. The models were tested on weakly supervised tasks related to morphology, biomarkers, and prognostication. The table below summarizes the top-performing models based on the average Area Under the Receiver Operating Characteristic Curve (AUROC) across all tasks.

Table 1: Performance Benchmark of Leading Pathology Foundation Models [12]

Foundation Model Model Type Key Pretraining Characteristics Avg. AUROC (All Tasks) Key Strengths
CONCH Vision-Language 1.17M image-caption pairs 0.71 Highest overall performance, top in morphology (0.77) and prognosis (0.63)
Virchow2 Vision-Only 3.1M Whole Slide Images (WSIs) 0.71 Top performer in biomarker tasks (0.73), close second overall
Prov-GigaPath Vision-Only Large-scale WSI dataset 0.69 Strong performance in biomarker prediction
DinoSSLPath Vision-Only Not Specified 0.69 High performance in morphology tasks (0.76)

The study also found that models trained on distinct cohorts learn complementary features. An ensemble of CONCH and Virchow2 outperformed individual models in 55% of tasks, demonstrating the value of model fusion when supported by diverse, high-quality data [12].

Validation on Independent Cohorts

Robust validation requires testing on external cohorts that were not part of the model's training data. A study on endometrial cancer subtyping compared foundation models against traditional Convolutional Neural Networks (CNNs) pretrained on ImageNet [15].

Table 2: External Validation Performance for Endometrial Cancer Subtyping [15]

Model Type Specific Model Cross-Validation Macro-AUC External Validation Macro-AUC Performance Drop
Foundation Model UNI2 with CLAM 0.847* 0.780 ~0.067
Foundation Model Virchow2 with CLAM 0.860 0.767 ~0.093
CNN EfficientNet-B7 0.829 0.667 ~0.162
CNN ResNet-50 0.785 0.634 ~0.151

Note: UNI2 cross-validation AUC is an estimate based on reported model range.

The results show that foundation models not only achieved higher performance but also demonstrated superior generalizability, with a significantly smaller performance drop when applied to the independent cohort. This underscores their robustness when deployed in real-world clinical settings with data from new sources [15].

The Scientist's Toolkit: Essential Research Reagents & Materials

Building and evaluating foundation models requires a suite of specialized tools and resources. The following table catalogs key solutions used in the featured experiments.

Table 3: Key Research Reagent Solutions for Foundation Model Benchmarking

Item Name Function/Description Example Use Case
STAMP Pipeline An open-source pipeline for tiling WSIs, feature extraction, and Multiple Instance Learning (MIL) [15]. Systematic benchmarking of encoders and aggregators for solid tumour analysis [15].
TransMIL A transformer-based aggregator that uses self-attention to model relationships between tissue tiles [15]. Slide-level prediction from tile-level embeddings in histopathology images [15].
CLAM (CLAM-MB) An attention-based MIL aggregator with instance-level supervision, enabling interpretability [15]. Weakly supervised classification on whole-slide images, providing attention scores over tiles [15].
HIA & Modified HIA (Histomics Image Analysis) Software for preprocessing whole-slide images, including tissue segmentation and tiling [15]. Preparing WSIs for analysis by tessellating them into patches and applying quality filters [15].
INCISIVE Framework A multi-dimensional framework for pre-validating the quality of cancer imaging and clinical metadata [27]. Ensuring data completeness, validity, consistency, integrity, and fairness in a federated repository [27].
TumorImagingBench A curated benchmark of public datasets for evaluating foundation models on quantitative radiographic phenotypes of cancer [25]. Standardized benchmarking of model robustness and interpretability in oncological imaging [25].

Advanced Experimental Protocols

To ensure reproducible and rigorous benchmarking, the following detailed methodologies are essential.

Protocol 1: Benchmarking Foundation Models on Multi-Center Data

This protocol is derived from the large-scale histopathology benchmarking study [12].

  • Objective: To evaluate and compare the performance of multiple foundation models on external, multi-center cohorts across clinically relevant tasks.
  • Data Curation:
    • Cohort Assembly: Collect data from multiple independent cohorts (e.g., 13 cohorts with 6,818 patients) that were not part of any foundation model's pretraining set to prevent data leakage.
    • Task Definition: Define weakly supervised prediction tasks related to morphology, biomarkers (e.g., BRAF mutation), and prognostication.
  • Feature Extraction & Aggregation:
    • Tessellate Whole-Slide Images (WSIs) into small, non-overlapping patches.
    • Extract tile-level embeddings using frozen, pre-trained foundation models (e.g., CONCH, Virchow2).
    • Aggregate tile embeddings into slide-level predictions using a Multiple Instance Learning (MIL) model, such as a transformer-based aggregator.
  • Evaluation:
    • Primary Metric: Area Under the Receiver Operating Characteristic Curve (AUROC).
    • Secondary Metrics: Area Under the Precision-Recall Curve (AUPRC), Balanced Accuracy, and F1 scores.
    • Statistical Analysis: Perform statistical tests to compare AUROCs across models for each task.

The workflow for this benchmarking protocol is illustrated below.

Start Multi-Center Patient Cohorts A WSI Tessellation into Patches Start->A B Tile-Level Feature Extraction A->B C Embedding Aggregation (MIL Model) B->C D Model Evaluation (AUROC, AUPRC, F1) C->D End Performance Benchmark & Model Ranking D->End

Protocol 2: Low-Data and Low-Prevalence Scenario Testing

This protocol assesses model utility in realistic settings where labeled data is scarce or positive cases are rare [12].

  • Objective: To analyze foundation model performance when downstream training data is limited or task prevalence is low.
  • Procedure:
    • From a full training cohort, create randomly sampled subsets (e.g., n=75, 150, and 300 patients) while maintaining the original ratio of positive samples.
    • Train downstream models exclusively on these subsets.
    • Validate the models on the full-size, held-out external cohort.
    • Compare performance metrics (AUROC) across different sample sizes and against models trained on the full dataset.
  • Outcome Analysis: Identify which foundation models maintain the highest performance in low-data regimes, providing insight into their data efficiency for rare molecular events.

The construction of high-quality, multi-center repositories through rigorous data curation and preprocessing is the non-negotiable foundation of trustworthy and effective cancer imaging AI. Independent benchmarking studies consistently demonstrate that even the most advanced foundation models, such as CONCH and Virchow2, are ultimately constrained by the quality and diversity of the data on which they are evaluated [12] [15]. The application of structured pre-validation frameworks, like the one pioneered by the INCISIVE project, is critical for exposing and mitigating data quality issues that lead to biased, non-robust, and unfair models [27]. As the field progresses, the choice of a foundation model remains important, but it is secondary to the meticulous work of building and maintaining the data repositories that fuel these models. Future efforts must prioritize data quality, standardization, and equitable representation to ensure that AI fulfills its promise of advancing precision oncology for all patients.

The emergence of foundation models trained on vast datasets of medical images has created a paradigm shift in computational pathology and cancer imaging biomarker discovery [29]. These models, pre-trained using self-supervised learning on extensive unannotated data, encapsulate rich, general-purpose representations of histopathological and radiological patterns. However, a critical challenge lies in effectively adapting these powerful base models to specific, clinically relevant downstream tasks such as biomarker prediction, cancer subtyping, and prognosis estimation [12] [30].

Within this context, two principal adaptation paradigms have emerged: feature extraction and full fine-tuning. Feature extraction involves using the pre-trained foundation model as a fixed feature extractor, then training a simple classifier (e.g., a linear layer) on these frozen representations. In contrast, full fine-tuning unfreezes and retrains all or most of the model's parameters on the new task data, allowing the model to adjust its foundational knowledge to the target domain [31] [32]. The systematic benchmarking of these strategies is paramount for researchers and drug development professionals who must make informed decisions about model adaptation to build robust, generalizable, and clinically actionable AI solutions for oncology.

Conceptual Frameworks and Key Distinctions

Understanding the fundamental operational differences between these strategies is prerequisite to their comparative benchmarking.

  • Feature Extraction: The foundation model's weights remain entirely frozen. Input images are passed through the network to extract high-level feature representations from a specific layer (typically the penultimate layer). These features then serve as input to a new, task-specific classifier trained from scratch. This approach is computationally efficient and less prone to overfitting, as the number of trainable parameters is substantially reduced [31] [32].

  • Full Fine-Tuning: This strategy involves initializing a model with pre-trained weights and then continuing the training process on the target dataset, updating all layer parameters. This allows the model to refine its general-purpose features to the specifics of the new domain, potentially achieving higher performance but requiring more data and computational resources to avoid catastrophic forgetting or overfitting [32] [33].

The following diagram illustrates the core architectural and dataflow differences between these two approaches.

G cluster_fe Feature Extraction Strategy cluster_ft Full Fine-Tuning Strategy Input1 Input Image FM1 Frozen Foundation Model Input1->FM1 Feat1 Extracted Feature Vector FM1->Feat1 CLF1 New Classifier (Trainable) Feat1->CLF1 Output1 Task Prediction CLF1->Output1 Input2 Input Image FM2 Foundation Model (All Layers Trainable) Input2->FM2 CLF2 Classifier Head (Trainable) FM2->CLF2 Output2 Task Prediction CLF2->Output2

Experimental Benchmarking Methodologies

Independent, large-scale benchmarking studies provide the most rigorous comparative data. The following section details a representative experimental protocol from a major study evaluating 19 foundation models for computational pathology, which directly compared feature extraction and fine-tuning performance across multiple cancer types and tasks [12].

Benchmarking Workflow for Strategy Evaluation

The standard benchmarking workflow involves a consistent pipeline from model selection and data preparation to task-specific evaluation, enabling a fair comparison between adaptation strategies.

G cluster_adapt Adaptation Strategies Start Pre-trained Foundation Model A Task & Dataset Definition Start->A B Model Adaptation A->B B1 Feature Extraction: Freeze backbone, train new head B->B1 B2 Full Fine-Tuning: Update all model parameters B->B2 C Model Evaluation D Performance Comparison C->D B1->C B2->C

Key Experimental Protocols

  • Model Selection and Preparation: Studies typically benchmark a diverse set of publicly available foundation models. A prominent benchmark in computational pathology evaluated 19 models, including vision-only (e.g., Virchow2) and vision-language (e.g., CONCH) architectures, pre-trained on datasets ranging from ~1 million to over 1.4 million whole-slide images [12]. Models are prepared for adaptation by either removing or keeping the final classification layer.

  • Datasets and Task Formulation: Benchmarking utilizes multiple external patient cohorts not seen during the models' pre-training to ensure independent validation and mitigate data leakage. For example, one study used 13 cohorts comprising 6,818 patients and 9,528 slides across lung, colorectal, gastric, and breast cancers [12]. Tasks are structured as weakly supervised problems, where slide-level labels are used to predict biomarkers (e.g., microsatellite instability), morphological properties (e.g., tumor grade), and prognostic outcomes (e.g., survival) [12] [34].

  • Implementation of Adaptation Strategies:

    • Feature Extraction: The foundation model backbone is frozen. Feature vectors are extracted, typically from the last layer before the classifier, for all image patches in a whole-slide image. These features are then aggregated using a multiple instance learning (MIL) framework, such as a transformer or attention-based MIL aggregator, to make a single slide-level prediction [12].
    • Full Fine-Tuning: All parameters of the foundation model are unfrozen. The model, often with a new task-specific head, is trained end-to-end on the target task. A lower learning rate is typically used to prevent drastic deviation from the pre-trained weights [32] [29].

  • Evaluation Metrics: Performance is measured using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), which are standard for medical classification tasks. Balanced accuracy and F1-score are also reported, especially for imbalanced datasets common in low-prevalence biomarker studies [12] [29].

The Scientist's Toolkit: Essential Research Reagents

The following table details key resources and their functions as derived from the benchmarked experiments.

Research Reagent / Resource Function in Experiment
Foundation Models (e.g., CONCH, Virchow2) Pre-trained encoders providing general-purpose feature representations from histopathology images [12].
Multiple Instance Learning (MIL) Aggregator Aggregates patch-level features or predictions into a single slide-level prediction for whole-slide image analysis [12].
Whole-Slide Image (WSI) Datasets Large-scale, multi-cancer cohorts with slide-level annotations for biomarkers, morphology, and prognosis [12] [34].
Linear Classifier A simple, trainable layer (e.g., fully connected) used on top of frozen features in the feature extraction paradigm [29].
Computational Framework (e.g., PyTorch/TensorFlow) Software environment for implementing feature extraction, fine-tuning pipelines, and MIL aggregation [32].

Comparative Performance Analysis

Quantitative benchmarking across diverse tasks reveals the nuanced performance landscape of each strategy. The tables below synthesize key findings from large-scale studies.

Table 1: Overall performance profile of feature extraction versus full fine-tuning.

Benchmarking Aspect Feature Extraction Full Fine-Tuning
Primary Use Case Small/similar datasets; rapid prototyping; low-data scenarios [31] [29]. Large/different datasets; maximum performance pursuit [31] [32].
Computational Cost Lower (frozen backbone, fewer trainable parameters) [32]. Higher (updates all parameters, requires more VRAM) [32] [33].
Risk of Overfitting Lower [31] [32]. Higher, necessitates careful regularization [31] [32].
Data Efficiency High performance with limited labeled data [12] [29]. Requires larger datasets to generalize effectively [32].
Representative Best AUROC 0.71 (CONCH model on 31 pathology tasks) [12]. 0.944 (Foundation model on lung nodule malignancy) [29].

Table 2: Performance comparison across different data regimes in medical imaging (Synthetic representation based on benchmark findings [12] [29]).

Data Regime Exemplar Task Feature Extraction (Best AUROC) Full Fine-Tuning (Best AUROC) Performance Insight
Low-Data (n=75-150) Rare biomarker prediction ~0.65 - 0.70 (e.g., PRISM, CONCH) [12] Not reported / Often inferior Feature extraction is highly competitive; fine-tuning risks overfitting.
Medium-Data Lung nodule malignancy ~0.91 (Foundation features) [29] 0.944 (Foundation fine-tuned) [29] Fine-tuning starts to show a performance advantage.
Full-Data Anatomical site classification 0.804 (Balanced Accuracy) [29] 0.779 (Balanced Accuracy) [29] Feature extraction can match or surpass fine-tuning, offering computational savings.

Critical Interpretations of Benchmarking Data

  • Performance Parity and Superiority in Specific Contexts: A key finding from benchmarks is that feature extraction can achieve performance on par with, and in some cases surpass, full fine-tuning. In a technical validation task of lesion anatomical site classification, a linear classifier on top of frozen features from a foundation model (Foundation (features)) significantly outperformed a fully fine-tuned version of the same model (Foundation (fine-tuned)) and other fine-tuned supervised baselines in mean Average Precision (mAP) [29]. This demonstrates that high-quality foundation models learn generally powerful representations that do not always require adjustment for in-distribution tasks.

  • The Low-Data Regime Advantage: Feature extraction consistently demonstrates a strong advantage in scenarios with limited annotated data, which is common for rare cancers or molecular biomarkers. Benchmarking revealed that in cohorts with as few as 75 patients, feature extraction with models like CONCH and PRISM led or performed competitively in the majority of tasks [12]. This is because it avoids overfitting by leveraging frozen, robust pre-trained features rather than attempting to update millions of parameters with minimal data.

  • Task-Dependent Performance Gaps: The margin by which fine-tuning outperforms feature extraction is task-dependent. For instance, in an out-of-distribution task predicting lung nodule malignancy, full fine-tuning of a foundation model achieved a significantly higher AUC (0.944) compared to using its extracted features [29]. This suggests that fine-tuning is particularly beneficial when the target task requires a non-trivial shift from the data distribution encountered during pre-training.

The choice between feature extraction and full fine-tuning is not a binary one but a strategic decision that depends on the specific research context and constraints. Based on the consolidated benchmarking evidence, the following decision framework is proposed for cancer imaging researchers.

G leaf leaf Start Start: Choosing an Adaptation Strategy Q1 Is your labeled dataset small or highly imbalanced? Start->Q1 Q2 Is the task or data distribution significantly different from pre-training? Q1->Q2 No A1 Use Feature Extraction Q1->A1 Yes Q3 Are computational resources (e.g., GPU memory) constrained? Q2->Q3 No A2 Use Full Fine-Tuning Q2->A2 Yes Q4 Is achieving the absolute highest performance the primary goal? Q3->Q4 No A3 Use Feature Extraction Q3->A3 Yes Q4->A2 Yes A4 Use Feature Extraction as a baseline, then try Fine-Tuning Q4->A4 No

Strategic Recommendations:

  • Default Starting Point: Given its computational efficiency and robustness, feature extraction should be the default starting point for benchmarking and developing new cancer imaging models. It establishes a strong performance baseline with minimal resource investment [12] [29].

  • Conditions for Fine-Tuning: Reserve full fine-tuning for scenarios where a large, high-quality annotated dataset is available, the task requires a significant domain shift from the pre-training data, and the marginal performance gain justifies the substantial computational cost and risk of overfitting [32] [29].

  • Ensemble and Hybrid Approaches: Benchmarking reveals that foundation models trained on distinct cohorts learn complementary features. Ensembling predictions from models adapted via different strategies (e.g., combining a feature-based CONCH model with a fine-tuned Virchow2) can outperform individual models, leveraging the strengths of both approaches [12].

In conclusion, the benchmarking of feature extraction versus full fine-tuning reveals a complex trade-off landscape governed by data, task, and resource constraints. For the cancer imaging researcher, a pragmatic, empirically grounded approach—beginning with feature extraction and progressing to fine-tuning only when necessary and viable—will yield the most efficient and effective path to clinically relevant model development.

The application of foundation models in cancer imaging research is often constrained by the limited availability of annotated medical data, a challenge pervasive in oncology domains ranging from radiology to histopathology. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a critical methodology that enables effective model adaptation under these low-data regimes by updating only a small subset of parameters, thus preventing overfitting while maintaining computational feasibility [35]. This guide provides a comprehensive comparative analysis of two prominent PEFT methods—Low-Rank Adaptation (LoRA) and Adapters—specifically within the context of benchmarking cancer imaging foundation models. We objectively evaluate their performance, resource efficiency, and implementation characteristics based on recent experimental studies to inform researchers, scientists, and drug development professionals in selecting optimal adaptation strategies for medical imaging applications.

Performance Comparison in Medical Imaging

Quantitative Performance Metrics

Experimental results across multiple cancer imaging domains demonstrate that both LoRA and Adapters can achieve competitive performance compared to full fine-tuning while using significantly fewer trainable parameters.

Table 1: Performance Comparison of PEFT Methods in Cancer Imaging Tasks

Method Application Context Performance Metric Result Parameter Efficiency Citation
LoRA Lung nodule malignancy classification (NLSTx, LIDC datasets) ROC AUC 3% increase over state-of-the-art 89.9% fewer parameters [36]
LoRA Lung nodule malignancy classification Training Time 36.5% reduction per iteration 85M fewer trainable parameters [36]
Adapters General NLP for medical text Task Performance Matches full fine-tuning BERT ~3.6% of parameters trained [35]
DoRA Multi-modal medical image segmentation (HECKTOR dataset) Dice Score +28% improvement on PET scans Only 8% trainable parameters [37]
QDoRA Medical vision tasks Benchmark Performance Matches or exceeds full fine-tuning ~2% of weights trained [35]
PEFT (LoRA/BitFit) COVID-19 outcome prediction from chest X-rays PR-AUC Competitive with full fine-tuning on larger datasets Significant parameter reduction [38]

Contextual Performance Analysis

The comparative effectiveness of LoRA and Adapters varies significantly based on dataset characteristics and task complexity. In scenarios with severe class imbalance, which is common in medical prognosis tasks, LoRA-adapted foundation models may experience performance degradation, whereas balanced data distributions mitigate this effect [38]. For multi-modal adaptation in cancer segmentation, DoRA (a LoRA variant) has demonstrated remarkable capability, improving Dice scores by 28% on PET scans when adapting a CT-pretrained model while using only 8% of the trainable parameters [37].

In low-data regimes specifically, adapters have shown strength in multi-task and continual learning scenarios common in medical applications, as they isolate task-specific knowledge and reduce catastrophic forgetting [35] [39]. However, for complex vision tasks requiring adaptation of large foundation models to specialized cancer imaging domains, LoRA and its variants ( particularly DoRA and QDoRA) have demonstrated superior performance in recent benchmarking studies [36] [37].

Experimental Protocols and Methodologies

LoRA for Lung Nodule Malignancy Classification

A rigorous experimental setup evaluated LoRA for adapting large vision models to lung nodule malignancy classification using the NLSTx and LIDC datasets, which collectively encompass diverse biopsy- and radiologist-confirmed lung CT scans [36].

Experimental Workflow:

G cluster_study Experimental Protocol Pretrained Pretrained LoRA LoRA Pretrained->LoRA Input Input Input->Pretrained Output Output LoRA->Output Malignancy Malignancy LoRA->Malignancy NLSTx NLSTx ThreeSlices ThreeSlices NLSTx->ThreeSlices LIDC LIDC LIDC->ThreeSlices ViT ViT ViT->LoRA ThreeSlices->ViT

Methodology Details:

  • Foundation Models: Self-supervised learning pretrained Vision Transformers (ViT) and Swin Transformers of varying sizes (small to giant) were adapted [36].
  • Input Configuration: Three orthogonal slices (mid-axial, mid-sagittal, mid-coronal) were extracted from each nodule center and assigned to three input channels to preserve 3D context for 2D vision models [36].
  • Adaptation Technique: LoRA was applied to the attention mechanisms of transformer blocks, freezing original weights and injecting low-rank decomposition matrices.
  • Training Protocol: Models were evaluated using cross-validation with 324 model variants representing combinations of input configurations, crop sizes, adaptation techniques, and model architectures.
  • Evaluation Metrics: ROC AUC, parameter efficiency (percentage of trainable parameters), and training time per iteration.

Adapters for Metastasis Detection from Radiology Reports

A comprehensive study investigated adapter-based fine-tuning for automatic identification of metastatic sites from radiology reports using limited gold-labeled data [40].

Experimental Workflow:

G cluster_study Adapter Implementation BaseModel BaseModel Adapter Adapter BaseModel->Adapter EHR EHR EHR->BaseModel Metastasis Metastasis Adapter->Metastasis Radiology Radiology Synthetic Synthetic Radiology->Synthetic BERT BERT Modular Modular BERT->Modular Detection Detection Modular->Detection Synthetic->BERT

Methodology Details:

  • Foundation Model: BERT architecture pretrained on general domain text, adapted using adapter modules inserted between transformer layers [40].
  • Data Augmentation: Limited labeled data was expanded using Llama3 to generate synthetic training samples through paraphrasing of radiology report impression sections [40].
  • Adapter Architecture: Small feedforward networks with down-projection, nonlinearity, and up-projection layers were inserted between transformer blocks while keeping base model parameters frozen.
  • Targeted Augmentation: Three selective data augmentation techniques were investigated that focused expansion on the most informative samples rather than applying vanilla augmentation.
  • Evaluation Framework: Models were assessed using F1-score for lung, liver, and adrenal gland metastases detection, comparing structured reports versus impression-only settings.

Technical Implementation Guide

Fundamental Architectural Differences

The core distinction between LoRA and Adapters lies in their integration approach with foundation models. Adapters introduce additional layers between existing transformer components, creating a sequential processing bottleneck. In contrast, LoRA operates through parallel integration, injecting low-rank decomposition matrices that modify weight matrices without adding sequential computational steps [41] [35].

Table 2: Architectural Comparison of LoRA and Adapters

Characteristic LoRA Adapters
Integration Approach Parallel low-rank decomposition of weight matrices Sequential addition of feedforward networks between layers
Parameter Efficiency Typically <1% of original parameters [35] ~3-4% of original parameters [35]
Inference Overhead Minimal after weight merging Slight latency due to additional layers
Modularity Multiple adapters can be trained and switched High modularity with task-specific adapters
Implementation Complexity Moderate (matrix decomposition) Low (standard feedforward networks)
Optimal Domains Vision transformers, large language models [36] [37] Multi-task learning, continual learning scenarios [35] [39]

Implementation Workflows

LoRA Implementation Protocol:

G cluster_lora LoRA Mathematical Foundation Frozen Frozen LoRAA LoRAA Frozen->LoRAA Input Input Input->Frozen LoRAB LoRAB LoRAA->LoRAB Output Output LoRAB->Output W Original Weights (W₀) A A W->A B B A->B DeltaW ΔW = BA B->DeltaW Updated h = W₀x + ΔWx DeltaW->Updated

The LoRA methodology freezes pre-trained model weights and injects trainable rank decomposition matrices into each layer of the transformer architecture. The forward pass is modified as: ( h = W0x + \Delta Wx = W0x + BAx ), where ( W_0 ) represents frozen pre-trained weights, and ( BA ) constitutes the low-rank adaptation with ( A ) initialized using a random Gaussian and ( B ) initialized to zero [42] [41]. The rank parameter determines the dimensionality of the low-rank matrices, with lower ranks (8 or 16) often providing optimal balance between performance and efficiency for medical imaging tasks [42].

Adapter Implementation Protocol:

G cluster_adapter Adapter Module Architecture Input Input Transformer Transformer Input->Transformer DownProject DownProject Transformer->DownProject NonLinear NonLinear DownProject->NonLinear UpProject UpProject NonLinear->UpProject Output Output UpProject->Output Frozen Base Model (Frozen) Down Down-Projection (d→r) Frozen->Down NonLin Non-Linearity (ReLU/GELU) Down->NonLin Up Up-Projection (r→d) NonLin->Up Residual Residual Connection Up->Residual

Adapter modules are implemented as small feedforward networks inserted between layers of a pre-trained model, typically consisting of a down-projection layer (reducing dimensionality from original dimension d to bottleneck dimension r), a nonlinear activation function (usually ReLU or GELU), and an up-projection layer (returning from r to d) [41] [35]. During training, all base model weights remain frozen with only adapter parameters updated, creating modular knowledge representations that can be extracted, interchanged, and dynamically plugged into the foundation model [41].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for PEFT in Cancer Imaging

Research Reagent Function Example Implementation
LoRA Rank (r) Parameter Controls dimensionality of low-rank adaptation; balances capacity vs. efficiency Lower ranks (8-16) often optimal for medical tasks; higher ranks for complex adaptations [42]
Adapter Bottleneck Dimension Determines hidden layer size in adapter modules; affects parameter efficiency Typical bottleneck ratios of 16:1 or 32:1 (original dimension to bottleneck) [41]
4-bit NormalFloat (NF4) Quantization Enables memory-efficient fine-tuning of extremely large models Used in QLoRA to store base model weights in compressed 4-bit format [42] [35]
Targeted Data Augmentation Expands limited medical datasets while preserving label integrity LLM-generated synthetic samples via paraphrasing of medical reports [40]
Paged Optimizers Manages memory across GPU and CPU for large model training Critical for QLoRA implementation with limited GPU memory [35]
Weight Decomposition (DoRA) Separates magnitude and direction components for enhanced adaptation DoRA outperforms LoRA in medical vision tasks by mimicking full fine-tuning patterns [42] [37]

The benchmarking analysis presented in this guide demonstrates that both LoRA and Adapters provide effective strategies for adapting foundation models to cancer imaging tasks under low-data regimes. LoRA and its variants (particularly DoRA and QDoRA) have shown remarkable success in medical vision applications, achieving performance comparable to or exceeding full fine-tuning while utilizing orders of magnitude fewer parameters [36] [37]. Adapters remain valuable for scenarios requiring high modularity, multi-task learning, and continual adaptation across multiple medical domains [41] [40].

Selection between these PEFT methodologies should be guided by specific research constraints: LoRA excels in vision transformer adaptation for classification and segmentation tasks with minimal inference overhead, while Adapters offer advantages in modular deployment scenarios and when catastrophic forgetting must be minimized. As cancer imaging research increasingly relies on foundation models, parameter-efficient fine-tuning strategies will play a pivotal role in enabling robust, clinically applicable AI systems without prohibitive computational demands.

Cancer diagnosis and treatment planning have been revolutionized by computational approaches that leverage artificial intelligence. The development of foundation models, trained on massive datasets, provides a powerful base for various downstream tasks in digital pathology. This guide benchmarks the performance of leading cancer imaging foundation models across three critical clinical domains: tumor classification, molecular subtyping, and survival prediction. Independent evaluations reveal significant performance variations among models, with vision-language architectures and ensembles frequently outperforming alternatives. We present comparative performance data, detailed experimental methodologies, and essential research tools to assist researchers and clinicians in selecting appropriate models for their specific cancer imaging applications.

Comparative Performance of Cancer Imaging Foundation Models

Independent benchmarking studies provide crucial performance comparisons across multiple foundation models. The following table summarizes the overall performance of leading models across key domains, based on a comprehensive evaluation of 19 foundation models on 31 clinical tasks using 6,818 patients and 9,528 slides [12].

Table 1: Overall Performance of Foundation Models Across Key Domains (Mean AUROC)

Foundation Model Model Type Morphology (5 tasks) Biomarkers (19 tasks) Prognostication (7 tasks) Overall (31 tasks)
CONCH Vision-Language 0.77 0.73 0.63 0.71
Virchow2 Vision-only 0.76 0.73 0.61 0.71
Prov-GigaPath Vision-only 0.74 0.72 0.60 0.69
DinoSSLPath Vision-only 0.76 0.68 0.59 0.69
UNI Vision-only 0.74 0.68 0.59 0.68
BiomedCLIP Vision-Language 0.73 0.66 0.61 0.66
PLIP Vision-Language 0.70 0.63 0.58 0.64

For molecular subtyping classification performance, the DeepCMS framework demonstrates robust results across multiple metrics as shown in the table below [43].

Table 2: DeepCMS Performance in Molecular Subtyping Classification

Metric Performance Evaluation Method
Accuracy >0.90 Independent test datasets
Sensitivity >0.90 Aggregated measures
Specificity >0.90 Aggregated measures
Balanced Accuracy >0.90 Aggregated measures

Experimental Protocols and Methodologies

Benchmarking Framework for Foundation Models

The comprehensive benchmarking study employed a standardized methodology to ensure fair comparison across models [12]. The protocol involved:

  • Dataset Composition: 13 patient cohorts with 6,818 patients and 9,528 slides from lung, colorectal, gastric, and breast cancers
  • Task Selection: 31 weakly supervised downstream prediction tasks (5 morphology, 19 biomarkers, 7 prognostication)
  • Model Evaluation: Area Under the Receiver Operating Characteristic Curve (AUROC) as primary metric, with additional analysis of Area Under the Precision-Recall Curve (AUPRC), balanced accuracy, and F1 scores
  • Feature Extraction: Whole-slide image (WSI) tessellation into non-overlapping patches with tile-level embedding extraction
  • Aggregation Method: Transformer-based multiple instance learning (MIL) approach compared with attention-based MIL (ABMIL)

The benchmarking revealed that vision-language model CONCH and vision-only model Virchow2 achieved the highest overall performance, with ensembles of complementary models further enhancing predictive accuracy [12].

CHIEF Model Development and Validation

The Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model employed a distinctive dual-pretraining approach [44]:

  • Unsupervised Pretraining: Tile-level feature representation learning using 15 million unlabeled pathology image tiles
  • Weakly Supervised Pretraining: Whole-slide level context representation using 60,530 WSIs across 19 anatomical sites
  • Model Architecture: Efficient tile-level feature aggregation framework for large-scale WSI analysis
  • Validation Scope: 32 independent datasets with 19,491 WSIs from 24 international hospitals and cohorts

This methodology enabled CHIEF to achieve a macro-average AUROC of 0.9397 across 15 cancer detection datasets representing 11 cancer types, outperforming specialized models like CLAM, ABMIL, and DSMIL by 10-14% [44].

Workflow for Foundation Model Benchmarking

The following diagram illustrates the standardized experimental workflow used for benchmarking foundation models across multiple domains:

G Start Input Whole-Slide Images Preprocessing Image Preprocessing & Tessellation Start->Preprocessing FeatureExtraction Feature Extraction Using Foundation Models Preprocessing->FeatureExtraction TaskSpecific Task-Specific Model Training (MIL Aggregation) FeatureExtraction->TaskSpecific Evaluation Performance Evaluation (AUROC, AUPRC, Accuracy) TaskSpecific->Evaluation Comparison Model Comparison & Ensemble Analysis Evaluation->Comparison

Performance Analysis Across Domains

Tumor Classification Performance

In cancer detection and classification, the CHIEF model demonstrated exceptional performance across multiple cancer types [44]:

  • Biopsy Datasets: AUROCs >0.96 across esophagus, stomach, colon, and prostate cancers
  • Surgical Resection Datasets: AUROCs >0.90 across breast, endometrium, lung, and cervix cancers
  • Attention Alignment: High agreement with pathologist-annotated ground truth at pixel level

For brain tumor classification specifically, advanced deep learning models including EfficientNet and Swin Transformers have achieved testing accuracies of 98.72% and 98.08% respectively, surpassing conventional CNNs (95.16% accuracy) [45].

Molecular Subtyping and Biomarker Prediction

Foundation models show strong capability in predicting molecular profiles from routine H&E-stained slides [12] [44]:

  • High-Performance Biomarkers: 9 genes predicted with AUROCs >0.8 in pan-cancer genetic mutation analysis
  • TP53 Mutation: Strong morphological signals consistent with prior studies
  • Clinically Actionable Markers: IDH status in glioma and microsatellite instability (MSI) in colorectal cancer

Genomic assays like MammaPrint and BluePrint provide molecular subtyping that enables personalized treatment planning, with BluePrint reclassifying 61.4% of conventionally classified HR+HER2+ tumors into different molecular subtypes [46].

Survival Prediction Capabilities

Machine learning methods increasingly outperform traditional statistical approaches for survival prediction [47] [48]:

  • Random Survival Forests: Superior performance for colon cancer survival prediction with concordance index of 0.8146
  • Key Predictive Factors: Age, treatment type, positive nodes, tumor stage, smoking, and comorbidities
  • Subgroup Performance: Consistent discrimination across early-age diagnosis (0.8175), late-age diagnosis (0.7841), and geographic regions

Multi-task and deep learning methods demonstrate particularly strong performance in survival analysis, though they remain underutilized in current literature [47].

Low-Data Scenario Performance

A critical consideration for clinical application is model performance in data-scarce environments, which mirrors real-world scenarios with rare molecular events [12]:

  • Small Cohort (n=75): CONCH led in 5 tasks, PRISM and Virchow2 each led in 4 tasks
  • Medium Cohort (n=150): PRISM dominated with 9 leading tasks, Virchow2 followed with 6 tasks
  • Large Cohort (n=300): Virchow2 demonstrated superior performance in 8 tasks, PRISM followed with 7 tasks

These findings indicate that while Virchow2 and PRISM excel with more data, CONCH maintains competitive performance in low-data scenarios, valuable for rare cancer subtypes or biomarkers.

The Researcher's Toolkit

Table 3: Essential Research Reagent Solutions for Cancer Imaging Experiments

Resource Category Specific Examples Function/Application
Foundation Models CONCH, Virchow2, CHIEF, Prov-GigaPath Feature extraction from histopathology images
Genomic Assays MammaPrint, BluePrint, ImPrintTN Molecular subtyping and risk stratification
Validation Frameworks ABMIL, Transformer-based MIL Model aggregation and validation
Datasets FLEX Study, TCGA, CPTAC, BRATS Training and validation data sources
Performance Metrics AUROC, AUPRC, Balanced Accuracy, C-index Model evaluation and comparison

Benchmarking studies reveal that no single foundation model dominates across all domains and scenarios. Vision-language model CONCH and vision-only model Virchow2 demonstrate the highest overall performance, with specific strengths in different contexts. Ensemble approaches combining complementary models consistently outperform individual models, achieving superior performance in 55% of tasks. For clinical applications, model selection must consider specific use cases, with factors like data availability, tissue type, and target biomarkers influencing optimal choice. As the field evolves, ongoing independent benchmarking remains essential for guiding model selection and development in computational pathology.

Navigating Practical Challenges: Data Scarcity, Imbalance, and Model Robustness

Artificial intelligence holds significant promise for improving prognosis prediction in medical imaging, yet its effective application is often challenged by the limited availability of high-quality annotated data, particularly in oncology [30]. This scarcity is especially pronounced when studying rare cancers, emerging biomarkers, or underrepresented patient populations, where collecting large datasets is impractical [49]. Few-shot learning (FSL) addresses this critical bottleneck by enabling models to learn new tasks from only a handful of examples, dramatically reducing dependency on large, curated datasets [50] [51].

Within computational pathology and radiology, foundation models—pre-trained on massive unlabeled datasets through self-supervised learning—have emerged as powerful feature extractors that can be adapted to specialized clinical tasks with minimal labeled examples [12] [30]. This capability makes them particularly valuable for biomarker prediction, morphological analysis, and prognostic outcome tasks in cancer imaging. However, not all foundation models perform equally well in data-scarce environments, necessitating systematic benchmarking to guide model selection for specific clinical applications [12].

This article presents a comprehensive comparison of foundation models and adaptation strategies for few-shot learning in cancer imaging, synthesizing evidence from recent large-scale benchmarks to inform researchers and drug development professionals about optimal approaches for leveraging AI in low-resource settings.

Benchmarking Foundation Models for Cancer Imaging

Comprehensive Performance Evaluation

Recent independent benchmarking efforts have rigorously evaluated pathology foundation models across diverse clinical tasks and patient cohorts. One landmark study assessed 19 foundation models on 13 patient cohorts with 6,818 patients and 9,528 slides across lung, colorectal, gastric, and breast cancers [12]. The models were evaluated on 31 clinically relevant tasks related to biomarkers, morphological properties, and prognostic outcomes using weakly supervised learning approaches.

Table 1: Overall Performance of Leading Pathology Foundation Models Across All Tasks

Foundation Model Model Type Pretraining Data Scale Mean AUROC (All Tasks) Mean AUROC (Morphology) Mean AUROC (Biomarkers) Mean AUROC (Prognosis)
CONCH Vision-Language 1.17M image-caption pairs 0.71 0.77 0.73 0.63
Virchow2 Vision-Only 3.1M whole slide images 0.71 0.76 0.73 0.61
Prov-GigaPath Vision-Only 171K whole slide images 0.69 - 0.72 -
DinoSSLPath Vision-Only - 0.69 0.76 - -
BiomedCLIP Vision-Language 15M image-caption pairs 0.66 - - 0.61

The benchmarking revealed that CONCH, a vision-language model trained on 1.17 million image-caption pairs, and Virchow2, a vision-only model trained on 3.1 million whole-slide images, jointly outperformed all other pathology foundation models across the three key domains of morphology, biomarkers, and prognostication [12]. Interestingly, CONCH achieved this superior performance despite being trained on significantly fewer data points than BiomedCLIP (1.17 million versus 15 million image-caption pairs), suggesting that data diversity and model architecture play more critical roles than sheer dataset size [12].

Performance in Low-Data Scenarios

A key consideration for clinical deployment is how these models perform under extreme data scarcity. When downstream models were trained on randomly sampled cohorts of 300, 150, and 75 patients while maintaining similar positive sample ratios, performance patterns shifted notably [12]:

Table 2: Model Performance Under Data Scarcity

Training Cohort Size Best Performing Model Number of Tasks Where Model Led
300 patients Virchow2 8 tasks
150 patients PRISM 9 tasks
75 patients CONCH 5 tasks

In the largest sampled cohort (n=300), Virchow2 demonstrated superior performance in 8 tasks, while the medium-sized cohort (n=150) saw PRISM dominating with 9 tasks. Notably, with only 75 patients, CONCH led in 5 tasks, with performance metrics remaining relatively stable between n=75 and n=150 cohorts [12]. This stability in low-data regimes is particularly promising for clinical applications involving rare cancers or molecular subtypes.

For radiographic tumor imaging, similar benchmarking efforts have emerged. The TumorImagingBench framework evaluates foundation models on six public datasets (3,244 scans) with varied oncological endpoints [25]. This standardized evaluation approach enables direct comparison of model robustness to common sources of variability in medical imaging, saliency-based interpretability, and mutual similarity of learned embedding representations across different architectures and pre-training strategies.

Methodologies for Few-Shot Learning in Medical AI

Core Few-Shot Learning Framework

Few-shot learning operates on an N-way-K-shot classification framework, where N represents the number of classes and K represents the number of labeled examples ("shots") provided for each class [50]. In this framework:

  • Support Set: Contains K labeled training samples for each of the N classes that the model uses to learn generalized representations [50]
  • Query Set: Contains one or more new examples for each class that the model must classify based on representations learned from the support set [50]

The model undergoes multiple training episodes, with each episode consisting of different class combinations to encourage generalization rather than memorization of specific classes [50]. This approach stands in contrast to conventional supervised learning, which typically requires many hundreds or thousands of labeled data points across many rounds of training [50].

fsl_framework Data Data SupportSet SupportSet Data->SupportSet QuerySet QuerySet Data->QuerySet Model Model SupportSet->Model N-way K-shot training QuerySet->Model Predictions Predictions Model->Predictions Loss Loss Predictions->Loss Compute divergence Loss->Model Update parameters

Diagram 1: Few-Shot Learning Framework

Key Technical Approaches

Metric-Based Learning

Metric-based approaches learn a feature space where similar instances are close together, enabling models to classify new examples based on distance metrics [52]:

  • Prototypical Networks: Compute a class prototype (centroid) for each class in the embedding space and classify new samples based on proximity to these prototypes [49] [52]
  • Matching Networks: Utilize an attention mechanism to compare new samples against a support set in an embedding space, functioning like a sophisticated k-nearest neighbors approach in a deep learning context [49]
  • Relation Networks: Learn a non-linear similarity function between query and support set examples instead of relying on predefined distance metrics [50]
Optimization-Based Methods
  • Model-Agnostic Meta-Learning (MAML): Optimizes for initial model parameters that can be quickly adapted to new tasks with few gradient updates [49] [52]
  • Parameter-Efficient Fine-Tuning (PEFT): Modifies only a small subset of task-specific parameters while keeping the backbone model frozen, significantly reducing computational requirements and mitigating overfitting [30]
Transfer Learning with Foundation Models

This approach leverages useful features and representations that a trained model has already learned [50]. One simple method fine-tunes a classification model to perform the same task for a new class through supervised learning on a small number of labeled examples [50]. More intricate approaches teach new skills through relevant downstream tasks to a model that has been pre-trained via self-supervised pretext tasks [50].

Experimental Protocols for Benchmarking Foundation Models

Large-Scale Pathology Model Evaluation

The benchmarking methodology for pathology foundation models employed a standardized approach across 31 clinically relevant tasks [12]:

  • Dataset Composition: 6,818 patients and 9,528 slides across lung, colorectal, gastric, and breast cancers
  • Task Categories: 5 morphology-related tasks, 19 biomarker-related tasks, 7 prognostic-related tasks
  • Evaluation Metrics: Area Under the Receiver Operating Characteristic curve (AUROC) as the primary metric, supplemented by Area Under the Precision-Recall Curve (AUPRC), balanced accuracy, and F1 scores
  • Validation Approach: External validation using proprietary cohorts from multiple countries never included in foundation model training to mitigate data leakage risks
  • Weakly Supervised Framework: Whole-slide images were divided into patches with tile-level embeddings aggregated using transformer-based multiple instance learning (MIL)

benchmark_workflow cluster_foundation_models Foundation Models (Feature Extractors) WSIs WSIs Tiling Tiling WSIs->Tiling FeatureExtraction FeatureExtraction Tiling->FeatureExtraction Non-overlapping patches Aggregation Aggregation FeatureExtraction->Aggregation Tile embeddings ModelEnsemble ModelEnsemble ModelEnsemble->FeatureExtraction Select optimal model Prediction Prediction Aggregation->Prediction MIL transformer Evaluation Evaluation Prediction->Evaluation AUROC/AUPRC CONCH CONCH Virchow2 Virchow2 BiomedCLIP BiomedCLIP OtherModels OtherModels

Diagram 2: Benchmarking Workflow for Foundation Models

Few-Shot Learning Experimental Design

For few-shot learning scenarios, the benchmarking implemented:

  • Data Sampling Strategy: Downstream models trained on randomly sampled cohorts of 300, 150, and 75 patients while maintaining similar positive sample ratios [12]
  • Low-Prevalence Focus: Specific evaluation on clinically relevant tasks with rare positive cases (>15%) in training cohorts to simulate real-world challenges [12]
  • Cross-Validation: Models validated on full-size external cohorts after training on sampled datasets to assess generalization capability [12]
  • Ensemble Methods: Combination of predictions from multiple foundation models (e.g., CONCH and Virchow2) to leverage complementary strengths [12]

Statistical Analysis

  • Performance Comparison: Statistical significance testing of AUROC differences across models using pairwise comparisons [12]
  • Correlation Analysis: Examination of relationships between downstream performance and pretraining dataset characteristics (size, diversity) [12]
  • Robustness Evaluation: Comparison of transformer-based aggregation with widely used attention-based multiple instance learning (ABMIL) approach to validate methodological choices [12]

Table 3: Research Reagent Solutions for Few-Shot Learning in Cancer Imaging

Resource Category Specific Tools/Models Function & Application
Pathology Foundation Models CONCH, Virchow2, Prov-GigaPath, DinoSSLPath Feature extraction from whole-slide images for downstream classification tasks
Radiology Foundation Models CT-FM, CheXagent, DiCoM, ELIXR Quantitative feature extraction from radiographic images for tumor phenotyping
Benchmarking Frameworks TumorImagingBench [25], Pathology FM Benchmark [12] Standardized evaluation platforms for comparing model performance across datasets and tasks
Parameter-Efficient Fine-Tuning Methods LoRA (Low-Rank Adaptation), BitFit, VeRA, IA3 [30] Adaptation of foundation models to specific tasks with minimal parameter updates
Meta-Learning Algorithms MAML (Model-Agnostic Meta-Learning), Prototypical Networks, Matching Networks Optimization for rapid adaptation to new tasks with limited labeled examples

Discussion & Future Directions

Key Findings for Clinical Deployment

The benchmarking evidence reveals several critical insights for researchers and drug development professionals:

  • Vision-Language Models Offer Strong Performance: CONCH's leading performance across multiple domains demonstrates the value of integrating visual and textual information during pretraining, even for vision-specific downstream tasks [12]
  • Data Diversity Over Volume: The superior performance of CONCH compared to BiomedCLIP despite significantly smaller training dataset highlights that data diversity and quality outweigh sheer volume for foundation model development [12]
  • Complementary Model Strengths: The fact that model ensembles (e.g., CONCH and Virchow2) outperformed individual models in 55% of tasks suggests that different foundation models capture complementary features, making ensemble approaches particularly valuable for critical applications [12]
  • Stable Performance in Low-Data Regimes: The relatively stable performance metrics between n=75 and n=150 patient cohorts provides reassurance that foundation models can maintain effectiveness even with severe data limitations [12]

Implementation Recommendations

For researchers implementing few-shot learning solutions in cancer imaging:

  • Prioritize Data Diversity: When collecting pretraining data, focus on diversity of tissue sites, cancer types, and demographic representation rather than simply maximizing sample count [12]
  • Leverage Model Ensembles: Combine predictions from multiple foundation models to capture complementary feature representations, particularly for high-stakes clinical predictions [12]
  • Select Architectures for Specific Scenarios: Choose vision-language models for tasks requiring integration of imaging and clinical context, and vision-only models for pure visual pattern recognition tasks [12]
  • Implement PEFT for Rapid Adaptation: Utilize parameter-efficient fine-tuning methods rather than full fine-tuning when adapting foundation models to new tasks with limited data [30]

As the field progresses, the integration of few-shot learning with emerging technologies like federated learning will enable collaborative model development across institutions while preserving data privacy [53]. Similarly, advances in self-supervised learning and generative AI for data augmentation will further enhance model capabilities in data-scarce environments [50] [49]. For drug development professionals and clinical researchers, these advancements promise more rapid and cost-effective development of AI tools for precision oncology, potentially accelerating biomarker discovery and treatment personalization for even the rarest cancer subtypes.

Class imbalance presents a fundamental challenge in biomedical AI research, particularly in cancer imaging where meaningful clinical outcomes like disease progression or rare cancer subtypes are inherently uncommon. This phenomenon systematically biases model development, leading to artificially inflated performance metrics that mask poor generalization on the minority class of primary clinical interest. The foundation model era, while offering unprecedented representational power, introduces new complexities in handling imbalanced distributions, as standard fine-tuning approaches often destabilize on underrepresented categories. This comparison guide objectively evaluates techniques for reliable rare event performance within cancer imaging, providing empirical frameworks for selecting appropriate methodologies across diverse data regimes and architectural paradigms.

Recent benchmarking studies reveal that the interaction between model architecture, fine-tuning strategy, and imbalance mitigation technique significantly influences final performance on rare events. No single approach demonstrates universal superiority; instead, the optimal combination depends on specific constraints around dataset size, computational resources, and clinical task requirements. This analysis synthesizes evidence from comprehensive studies of foundation models and convolutional networks across multiple cancer domains to establish principled guidelines for robust model development in imbalanced settings.

Technical Approaches for Class Imbalance Mitigation

Algorithmic-Level Strategies

Data-level methods directly manipulate training set composition to balance class distributions. Oversampling techniques, including SMOTE and its derivatives, generate synthetic minority class instances, while undersampling reduces majority class representation. In cancer imaging, sophisticated augmentation strategies employing generative adversarial networks (GANs) or diffusion models can create realistic synthetic tissue samples, though these require careful validation to ensure biological plausibility [54]. Studies demonstrate that while basic oversampling improves recall on rare events, it may increase false positives without complementary regularization.

Algorithm-level methods modify the learning process to increase sensitivity to minority classes. Cost-sensitive learning assigns higher penalties for misclassifying rare events, effectively directing model attention toward under-represented categories. Focal loss dynamically scales cross-entropy based on prediction confidence, progressively focusing on harder examples during training [55]. Recent adaptations incorporate class-distance awareness, further refining minority class separation in embedding space. These approaches prove particularly valuable when data manipulation is infeasible due to privacy concerns or extremely limited samples.

Hybrid approaches combine data and algorithm-level strategies, such as applying cost-sensitive objectives to augmented datasets. Evidence suggests hybrid methods generally outperform either approach alone, with the specific optimal combination depending on the degree of imbalance and dataset size [54].

Architectural Considerations for Foundation Models

The emergence of foundation models has reshaped imbalance mitigation strategies, as their massive parameter counts and pretraining regimes introduce unique considerations. Parameter-Efficient Fine-Tuning (PEFT) methods, including LoRA (Low-Rank Adaptation) and BitFit, demonstrate particular value in low-data regimes by preventing catastrophic forgetting of features relevant to rare classes learned during pretraining [38].

Comprehensive benchmarking reveals a critical trade-off: convolutional neural networks (CNNs) with full fine-tuning maintain robustness on small, imbalanced datasets, while foundation models with PEFT achieve superior performance on larger datasets but struggle with severe imbalance [38]. This suggests a transitional point where foundation models become preferable, contingent on applying appropriate imbalance techniques.

G cluster_data Data-Level Methods cluster_algorithm Algorithm-Level Methods cluster_architectural Architectural Strategies Class Imbalance Problem Class Imbalance Problem Oversampling Oversampling Class Imbalance Problem->Oversampling Cost-Sensitive Learning Cost-Sensitive Learning Class Imbalance Problem->Cost-Sensitive Learning PEFT Methods PEFT Methods Class Imbalance Problem->PEFT Methods Hybrid Approach Hybrid Approach Oversampling->Hybrid Approach Undersampling Undersampling Synthetic Generation Synthetic Generation Cost-Sensitive Learning->Hybrid Approach Focal Loss Focal Loss Class-Weighted Loss Class-Weighted Loss Foundation Model Deployment Foundation Model Deployment PEFT Methods->Foundation Model Deployment Model Fusion Model Fusion Grad-CAM Explanations Grad-CAM Explanations Performance Evaluation Performance Evaluation Hybrid Approach->Performance Evaluation Foundation Model Deployment->Performance Evaluation

Figure 1: Technical taxonomy of class imbalance mitigation approaches, highlighting integration points between method categories and their path to performance evaluation.

Comparative Performance Analysis

Quantitative Benchmarking Across Cancer Types

Comprehensive evaluation across cancer domains reveals consistent patterns in technique effectiveness relative to data constraints and model architectures. The following table synthesizes performance metrics from recent large-scale benchmarking studies:

Table 1: Performance comparison of imbalance mitigation techniques across cancer imaging tasks

Technique Model Architecture Cancer Type Performance Metrics Data Regime
Class-weighted loss + FFT CNN (ResNet) COVID-19 CXR prognosis MCC: 0.42, PR-AUC: 0.68 Small, imbalanced (≤1,000 samples)
LoRA PEFT Vision Transformer Lung cancer (histopathology) AUROC: 0.73, F1: 0.71 Medium (3,000-10,000 samples)
BitFit PEFT Vision Transformer Colorectal cancer AUROC: 0.72, F1: 0.69 Medium (3,000-10,000 samples)
Model Fusion + Grad-CAM CNN (DenseNet121+Xception+VGG16) Breast cancer (ultrasound) Accuracy: 97%, F1: 0.96 Large (>10,000 samples)
Dynamic rescaled activation (BSSAF) Custom MLP Breast cancer F1: 0.99, Precision: 0.98 Imbalanced (minority class <10%)
Linear Probing Vision Transformer (CLIP) COVID-19 CXR prognosis MCC: 0.38, PR-AUC: 0.63 Few-shot (<100 samples per class)

Foundation models consistently achieve top performance in medium-to-large data regimes, with CONCH and Virchow2 demonstrating particular robustness across diverse cancer types including lung, colorectal, and breast cancers [12]. However, their advantage diminishes in severely imbalanced or low-data scenarios, where specialized CNNs with class-weighted losses maintain superior reliability. The Binary-Scaled Sigmoid Activation Function (BSSAF) demonstrates exceptional performance on highly imbalanced breast cancer classification, addressing vanishing gradient problems through dynamic rescaling [55].

Metric Selection for Reliable Evaluation

Performance assessment on imbalanced datasets requires careful metric selection to avoid misleading conclusions. Accuracy proves particularly problematic, as naive models can achieve high scores while completely failing to identify rare events. Studies indicate that AUC reliability in rare event settings depends primarily on the absolute number of events rather than event rate, with approximately 1,000 events needed for stable estimation [56].

Table 2: Evaluation metric behavior in rare event scenarios

Metric Sensitivity to Imbalance Key Dependency Reliability Threshold Clinical Interpretation
AUC Low Number of events ~1,000 events Overall ranking performance
Precision High Event rate Varies with prevalence False positive rate
Recall/Sensitivity High Number of events Minimum 50-100 events Case finding ability
F1-score High Balance of PPV and sensitivity Varies with prevalence Harmonic mean of precision/recall
MCC Low All confusion matrix cells ~100 events of each class Balanced measure for both classes
PR-AUC High Event rate Varies with prevalence Performance when positives are rare

Matthews Correlation Coefficient (MCC) and Precision-Recall AUC (PR-AUC) provide more reliable guidance for model selection in imbalanced contexts, with MCC offering particular value when both classes are clinically important [38] [57]. Precision becomes highly unstable in low-prevalence settings, fluctuating dramatically with minor prediction changes, while recall stability depends primarily on the absolute number of positive cases [56].

Experimental Protocols and Methodologies

Benchmarking Foundation Models with PEFT

Recent comprehensive benchmarking of foundation models and parameter-efficient fine-tuning for prognosis prediction establishes rigorous methodology for imbalance scenarios [38]. The experimental protocol employs four COVID-19 chest X-ray datasets with varying sample sizes (142-2,580 studies) and class imbalance ratios (6.5%-49.2% positive cases for mortality, severity, and ICU admission outcomes).

Dataset Preparation: All images undergo standardized preprocessing including resizing to uniform dimensions, intensity normalization, and data augmentation (random rotations, flips, and contrast adjustments). The benchmarking implements strict separation between training, validation, and test sets with patient-level partitioning to prevent data leakage.

Model Adaptation: The study compares CNN architectures (ResNet variants) pretrained on ImageNet against foundation models (CLIP, MedCLIP, BioMedCLIP) using three adaptation strategies: Full Fine-Tuning (FFT), Linear Probing (LP), and Parameter-Efficient Fine-Tuning (LoRA, BitFit). Class imbalance mitigation employs two approaches: (1) class-weighted loss functions that inversely weight classes by their frequency, and (2) balanced batch sampling that ensures equal representation per batch.

Evaluation Protocol: Models undergo evaluation under both full-data and few-shot regimes (1, 5, and 10 samples per class) using 5-fold cross-validation. Primary metrics include Matthews Correlation Coefficient (MCC) and Precision-Recall AUC (PR-AUC), with additional analysis of calibration and computational efficiency.

Explainable AI Integration for Model Validation

The Deep Neural Breast Cancer Detection (DNBCD) framework implements explainable AI components to validate model focus on clinically relevant features, particularly important for establishing trust in imbalance scenarios [54]. The methodology employs DenseNet121 as a foundational architecture enhanced with customized convolutional layers and transfer learning.

Architecture Details: The model incorporates GlobalAveragePooling2D, Dense (256 units, ReLU activation), and Dropout (0.4 rate) layers before final classification. Class imbalance specifically addresses through computed class weights applied to the categorical cross-entropy loss function, dynamically adjusting during training to prioritize minority class correctness.

Explainability Integration: Gradient-weighted Class Activation Mapping (Grad-CAM) generates visual explanations highlighting regions influencing predictions. This verification step ensures models learn meaningful pathological features rather than exploiting spurious correlations – a critical validation in imbalanced datasets where models frequently develop shortcut learning behaviors.

Training Protocol: The implementation uses Adam optimizer with learning rate 2e-5, batch size 16, and early stopping based on validation loss plateau. Evaluation employs both histopathological (Breakhis-400x) and ultrasound (BUSI) datasets, with rigorous train-test splits (70%-20%-10%) repeated across multiple random seeds.

G cluster_input Input Data cluster_processing Processing Stage cluster_model Model Adaptation cluster_output Output & Validation Medical Images Medical Images Data Augmentation Data Augmentation Medical Images->Data Augmentation Class Imbalance Class Imbalance Class Weighting Class Weighting Class Imbalance->Class Weighting Balanced Sampling Balanced Sampling Class Imbalance->Balanced Sampling Backbone Architecture Backbone Architecture Data Augmentation->Backbone Architecture Loss Optimization Loss Optimization Class Weighting->Loss Optimization Balanced Sampling->Backbone Architecture PEFT Methods PEFT Methods Backbone Architecture->PEFT Methods PEFT Methods->Loss Optimization Prediction Prediction Loss Optimization->Prediction XAI Interpretation XAI Interpretation Prediction->XAI Interpretation Metric Calculation Metric Calculation Prediction->Metric Calculation Clinical Validation Clinical Validation XAI Interpretation->Clinical Validation Performance Benchmarking Performance Benchmarking Metric Calculation->Performance Benchmarking

Figure 2: Experimental workflow for benchmarking foundation models on imbalanced medical imaging data, highlighting integration points for class imbalance mitigation throughout the pipeline.

Research Reagent Solutions

Table 3: Essential research reagents and computational resources for imbalance mitigation experiments

Resource Category Specific Tools Function in Research Implementation Notes
Foundation Models CONCH, Virchow2, BioMedCLIP, DenseNet121 Base architectures for transfer learning CONCH excels in vision-language tasks; Virchow2 strong vision-only performer [12]
PEFT Methods LoRA, BitFit, Adapter Parameter-efficient adaptation LoRA optimal for vision transformers; BitFit effective for smaller data [38]
Explanation Frameworks Grad-CAM, Grad-CAM++, SHAP Model decision interpretation Grad-CAM++ provides finer localization than Grad-CAM [58]
Evaluation Metrics MCC, PR-AUC, F1-score Robust performance assessment MCC preferred over accuracy for imbalance [38] [57]
Data Augmentation TensorFlow, PyTorch transforms Dataset expansion and balancing Crucial for minority class representation [54]
Class Imbalance Libraries imbalanced-learn, scikit-learn Algorithmic imbalance mitigation SMOTE variants for synthetic sample generation

This comparison guide establishes that effective class imbalance mitigation requires thoughtful integration of data, algorithmic, and architectural strategies tailored to specific data regimes and clinical tasks. CNN architectures with class-weighted losses maintain robust performance in low-resource scenarios, while foundation models with parameter-efficient fine-tuning techniques (LoRA, BitFit) achieve superior results when sufficient data exists. The Binary-Scaled Sigmoid Activation Function presents promising advances for addressing fundamental gradient instability in imbalanced learning.

Critical evaluation practices – particularly metric selection with emphasis on MCC and PR-AUC, and explainability integration through Grad-CAM – prove essential for reliable performance assessment in rare event scenarios. As foundation models continue to evolve, their effective application to imbalanced cancer imaging tasks will increasingly depend on specialized adaptation strategies that preserve sensitivity to clinically crucial rare events while leveraging their substantial representational power.

The performance of artificial intelligence (AI) in cancer imaging is critically dependent on the quality, standardization, and fairness of the underlying data. As foundation models—AI systems pre-trained on broad datasets—gain prominence in medical imaging, robust validation frameworks are essential to ensure these models are reliable and equitable when applied in clinical settings [27] [30]. High-quality, representative data is the cornerstone of developing trustworthy AI that can generalize across diverse patient populations and clinical scenarios. Challenges such as data heterogeneity, inconsistent formatting, and imbalanced representation of demographic or clinical subgroups can significantly compromise model performance and lead to biased outcomes [27] [59].

This guide explores a structured, multi-dimensional approach to data validation, benchmarking it against other common practices in the field. We will dissect the experimental protocols used to evaluate data quality, present performance comparisons of models built on validated data, and provide a toolkit for researchers to implement these frameworks in their own work.

A Multi-Dimensional Data Validation Framework

The INCISIVE project, an EU-funded initiative to create a pan-European repository of cancer images, proposes a comprehensive data validation framework designed to pre-validate clinical metadata and imaging data before their use in AI development [27] [60]. This framework assesses data across five core dimensions, serving as a robust alternative to ad-hoc data checking methods.

Table 1: The Five-Dimensional INCISIVE Validation Framework

Dimension Description Common Data Issues Identified
Completeness Assesses the presence of all required data elements. Missing clinical information or image annotations [27].
Validity Checks if data values conform to predefined formats and ranges. Inconsistent date formatting or out-of-range numerical values [27].
Consistency Ensures data is uniform and non-contradictory across the repository. Discrepancies in units of measurement or conflicting clinical descriptors [27].
Integrity & Uniqueness Verifies data relationships and removes duplicates. Duplicate patient records or broken links between images and their metadata [27].
Fairness Evaluates balanced representation of key demographic and clinical subgroups. Under-representation of specific age groups, sexes, or cancer types, which can lead to model bias [27].

The framework employs dedicated procedures for each dimension, including deduplication, annotation verification, DICOM metadata analysis, and anonymization compliance [27]. In the INCISIVE project, this process identified critical issues such as subgroup imbalances and inconsistent formatting, while also demonstrating the value of structured data entry and standardized protocols for ensuring data interoperability and equity [27].

The workflow below illustrates how this multi-dimensional validation integrates into the data curation pipeline for a cancer imaging repository.

Figure 1: Multi-Dimensional Data Validation Workflow start Raw Multicentric Data Ingestion dim1 Completeness Check start->dim1 dim2 Validity Check dim1->dim2 dim3 Consistency Check dim2->dim3 dim4 Integrity & Uniqueness Check dim3->dim4 dim5 Fairness Check dim4->dim5 decide Data Meets All Quality Thresholds? dim5->decide success Validated Data for AI Model Development decide->success Yes fail Quarantine & Remediation decide->fail No

Benchmarking Foundation Model Performance

Independent benchmarking on diverse, clinically relevant tasks is crucial for evaluating the real-world utility of foundation models. A large-scale study benchmarked 19 histopathology foundation models on 31 tasks across lung, colorectal, gastric, and breast cancers, involving 6,818 patients and 9,528 whole-slide images [12]. The tasks were grouped into predicting morphological properties, biomarkers, and prognostic outcomes.

Table 2: Benchmarking Performance of Select Pathology Foundation Models

Foundation Model Model Type Mean AUROC (Morphology) Mean AUROC (Biomarkers) Mean AUROC (Prognosis) Overall Mean AUROC
CONCH Vision-Language 0.77 0.73 0.63 0.71
Virchow2 Vision-Only 0.76 0.73 0.61 0.71
Prov-GigaPath Vision-Only - 0.72 - 0.69
DinoSSLPath Vision-Only 0.76 - - 0.69
BioMedCLIP Vision-Language - - 0.61 0.66

AUROC: Area Under the Receiver Operating Characteristic Curve. A higher value indicates better model performance. Data sourced from [12].

The results show that CONCH, a vision-language model, and Virchow2, a vision-only model, achieved the highest overall performance [12]. Furthermore, the study revealed that foundation models trained on distinct cohorts can learn complementary features. An ensemble model combining CONCH and Virchow2 outperformed individual models in 55% of tasks, demonstrating the power of leveraging multiple models to achieve state-of-the-art results [12].

Performance in Data-Scarce and Low-Prevalence Scenarios

A key promise of foundation models is their ability to perform well with limited labeled data, which is critical for modeling rare diseases. Benchmarking studies often evaluate this through few-shot learning (FSL) scenarios [30] [12]. In one benchmark, models were tested on tasks with very few positive cases, such as predicting BRAF mutation (10% prevalence) [12]. While CONCH and Virchow2 led in overall performance, other models like PRISM demonstrated superior performance in very low-data settings (e.g., with training cohorts of only 75 patients) [12]. This highlights that the optimal model choice can depend on the specific data availability of the clinical task.

Experimental Protocols for Validation and Benchmarking

Implementing rigorous validation and benchmarking requires standardized methodologies. Below are the core protocols derived from the cited literature.

Protocol for Multi-Dimensional Data Pre-Validation

This protocol is based on the INCISIVE project's framework for curating a federated cancer imaging repository [27].

  • Data Ingestion: Collect multimodal data (e.g., CT, MRI, PET-CT) from hospital PACS and clinical systems in DICOM and standard formats.
  • Dimension-Specific Checks:
    • Completeness: Scripts to identify missing fields in clinical metadata (e.g., patient age, cancer stage) or missing image series.
    • Validity: Rule-based checks to ensure data values fall within expected ranges (e.g., plausible dates, valid DICOM tags).
    • Consistency: Cross-reference data to flag inconsistencies (e.g., conflicting cancer grades in different documents).
    • Integrity & Uniqueness: Perform deduplication based on patient and study identifiers. Verify referential integrity between images and metadata.
    • Fairness: Analyze dataset demographics (sex, age) and clinical characteristics (cancer type, grade) to identify significant subgroup imbalances.
  • Anonymization & De-identification: Apply standardized tools to remove Protected Health Information (PHI) from DICOM headers and pixel data, ensuring compliance with regulations.
  • Annotation Verification: For data with expert annotations (e.g., tumor segmentation), perform checks for inter-observer variability and logical errors.
  • Output: Generate a quality report and a curated, analysis-ready dataset.

Protocol for Benchmarking Foundation Models

This protocol is adapted from large-scale independent benchmarks in computational pathology [12] and medical imaging [30].

  • Task Selection: Define a diverse set of clinically relevant tasks, such as biomarker prediction, morphological classification, and prognosis (e.g., survival prediction).
  • Model Selection: Include a wide array of publicly available foundation models, covering both general-purpose (e.g., CLIP) and biomedical-specific (e.g., BioMedCLIP, CONCH, Virchow2) models, as well as different architectural types (vision-only vs. vision-language) [30] [12].
  • Feature Extraction & Aggregation: For whole-slide images, tessellate them into small patches. Use each foundation model as a feature extractor to generate embeddings for each patch. Aggregate patch-level embeddings into a slide-level representation using an attention-based multiple instance learning (ABMIL) or transformer aggregator [12].
  • Downstream Model Training: Train a task-specific downstream classifier (e.g., a linear model or a simple neural network) on the aggregated features. To simulate real-world data scarcity, perform evaluations with varying training set sizes (e.g., 75, 150, 300 patients) [12].
  • Evaluation: Use robust metrics like Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC). Report results on held-out external test cohorts to ensure generalizability. Statistical testing (e.g., t-tests) should be used to compare model performance [12].

The Scientist's Toolkit: Essential Research Reagents

The following table details key resources, datasets, and models that are foundational for research in this field.

Table 3: Key Research Reagents and Resources

Item Name Type Function/Brief Explanation
INCISIVE Validation Framework Methodological Framework A structured procedure for pre-validating cancer imaging data across completeness, validity, consistency, integrity, and fairness [27].
MedFMC Dataset Benchmark Dataset A public dataset with 22,349 images across 5 clinical tasks (e.g., thoracic disease, pathology) designed to evaluate the generalizability of foundation models and few-shot learning methods [61].
CONCH Foundation Model A vision-language model trained on 1.17 million image-caption pairs, shown to achieve top performance in various pathology tasks including morphology, biomarkers, and prognosis [12].
Virchow2 Foundation Model A vision-only foundation model trained on 3.1 million whole-slide images, performing on par with CONCH in overall benchmarking [12].
DICOM Anonymizer Tool Software Tool A tool (e.g., from RSNA MIRC) used to remove patient identifiers from DICOM files, protecting privacy and enabling data sharing for research [61].
Parameter-Efficient Fine-Tuning (PEFT) Computational Method Techniques like LoRA (Low-Rank Adaptation) that fine-tune large foundation models with minimal parameters, reducing computational cost and overfitting in data-scarce medical tasks [30].

The move towards data-centric AI underscores that model performance is intrinsically linked to data quality and fairness. The INCISIVE multi-dimensional framework provides a rigorous, transferable model for ensuring that cancer imaging repositories are fit for purpose, directly addressing challenges of heterogeneity and bias that can undermine AI reliability [27]. Independent benchmarking, as demonstrated in large-scale pathology studies, is equally critical. It reveals that while top-performing models like CONCH and Virchow2 set a high bar, the "best" model is context-dependent, influenced by task specificity, data availability, and the potential of ensembles to combine complementary strengths [12].

For researchers and drug development professionals, the path forward involves adopting these structured validation protocols, leveraging public benchmarks for model selection, and prioritizing data diversity and equity from the outset. By doing so, the field can accelerate the development of robust, fair, and clinically trustworthy AI tools for cancer care.

Optimizing Computational Efficiency and Resource Demands for Clinical Deployment

The translation of cancer imaging foundation models from research to clinical practice is fundamentally constrained by computational efficiency and resource demands. While benchmark studies often focus on diagnostic accuracy, real-world deployment in hospitals and research laboratories depends critically on a model's computational footprint, inference speed, and adaptability to limited data scenarios [62] [63]. The pursuit of ever-larger models trained on massive datasets must be balanced against the practical realities of clinical hardware, data privacy requirements, and the need for rapid results that inform patient care [12] [29]. This comparative guide objectively evaluates leading foundation models across these critical operational dimensions, providing experimental data and methodologies to help researchers and developers select and optimize models for viable clinical integration.

The challenge is particularly acute in precision oncology, where clinical decision-making is often time-sensitive and healthcare institutions may lack specialized computational infrastructure [62] [3]. Furthermore, effective models must generalize across diverse patient populations and imaging protocols while operating efficiently, a dual requirement that demands careful architectural and training strategy selection [29] [25]. This analysis synthesizes recent benchmarking studies to identify which models achieve the optimal balance between performance and practical efficiency across key cancer imaging tasks.

Performance and Efficiency Benchmarking

Comprehensive Model Performance Across Cancer Types

Independent benchmarking studies reveal significant performance variations among pathology foundation models across different cancer types and tasks. The most comprehensive evaluation to date assessed 19 foundation models on 31 clinically relevant tasks across lung, colorectal, gastric, and breast cancers, encompassing 6,818 patients and 9,528 whole slide images [12].

Table 1: Overall Performance Benchmark of Leading Pathology Foundation Models

Foundation Model Model Type Pretraining Data Scale Mean AUROC (All Tasks) Key Performance Strengths
CONCH Vision-Language 1.17M image-caption pairs 0.71 Highest performer in morphology (AUROC 0.77) and prognostication (AUROC 0.63) tasks
Virchow2 Vision-Only 3.1M whole slide images 0.71 Top performer in biomarker prediction (AUROC 0.73), close second in other domains
Prov-GigaPath Vision-Only 171,000 whole slide images 0.69 Strong overall performance, particularly in biomarker prediction
DinoSSLPath Vision-Only Not specified 0.69 Competitive performance across all task types
UNI Vision-Only 100,000+ whole slide images 0.68 Balanced performance with relatively lower computational requirements

The benchmarking data indicates that vision-language models like CONCH can achieve performance competitive with larger vision-only models while leveraging multi-modal training [12]. Notably, Virchow2 demonstrates that extremely large-scale vision-only pretraining (3.1 million WSIs) yields robust performance across diverse tasks, though with potentially higher computational costs during pretraining.

Specialized Task Performance for Cancer Imaging

For specific clinical applications in oncology, model performance varies significantly based on task requirements and cancer type. Recent rigorous validation studies provide insights into optimal model selection for specialized diagnostic tasks.

Table 2: Specialized Task Performance Across Cancer Types

Clinical Application Best Performing Model(s) Performance Metrics Dataset Size Key Findings
Ovarian Carcinoma Subtyping H-optimus-0 Balanced Accuracy: 89% (internal), 97% (Transcanadian), 74% (OCEAN) 1,864 WSIs UNI achieved similar results at a quarter of the computational cost [64]
Endometrial Cancer Molecular Subtyping UNI2 with CLAM Macro-AUC: 0.780 (external validation) 815 patients (discovery), 720 patients (validation) Foundation models significantly outperformed CNNs in external validation (Macro-AUC 0.667-0.780 vs. lower for CNNs) [15]
Lung Tumor Segmentation MedSAM 2 Dice Score: 0.885 (fine-tuned) 2 lung tumor datasets Outperformed traditional models (U-Net: 0.817, nnUNet: 0.851) with greater computational efficiency [65]
Lung Nodule Malignancy Prediction Foundation (fine-tuned) AUC: 0.944, mAP: 0.953 507 lung nodules (LUNA16) Significant superiority over most baseline implementations (P < 0.01) [29]

These results demonstrate that while certain models achieve top performance in specific domains, computational efficiency varies significantly. For ovarian cancer subtyping, UNI provided nearly equivalent performance to H-optimus-0 with substantially lower computational requirements, highlighting the importance of evaluating this trade-off for clinical deployment [64].

Experimental Protocols and Methodologies

Benchmarking Framework Design

The most informative benchmarking studies employ rigorous methodologies that simulate real-world clinical challenges. The comprehensive assessment of 19 foundation models utilized a standardized evaluation framework across multiple external cohorts that were never part of any foundation model training, effectively mitigating the risk of data leakage from pretraining datasets [12]. The evaluation encompassed weakly supervised downstream prediction tasks related to morphology (5 tasks), biomarkers (19 tasks), and prognostication (7 tasks), using a total of 6,818 patients and 9,528 slides across lung, colorectal, gastric, and breast cancers [12].

For each model, the benchmarking protocol involved:

  • Feature Extraction: Using frozen foundation model weights to encode whole slide image patches into feature embeddings
  • Model Aggregation: Applying multiple instance learning (MIL) approaches with transformer-based aggregation or attention-based MIL (ABMIL)
  • Evaluation: Assessing performance using area under the receiver operating characteristic curve (AUROC) as the primary metric, with additional analysis of area under the precision-recall curve (AUPRC), balanced accuracy, and F1 scores
  • Statistical Analysis: Conducting pairwise comparisons between models with significance testing (P < 0.05) to determine performance differences [12]

This standardized approach enabled direct comparison across diverse model architectures and training paradigms, providing the community with reproducible evaluation metrics.

Computational Efficiency Assessment

Beyond raw performance, benchmarking computational efficiency requires standardized measurement of resource consumption across different operational scenarios. The segmentation benchmarking study compared traditional architectures (U-Net, DeepLabV3, nnUNet) against foundation models (MedSAM, MedSAM 2) across multiple efficiency dimensions [65]:

  • Few-shot Learning Capability: Evaluating performance with limited training data (25%, 50%, 75% of training set)
  • Inference Speed: Measuring processing time per image across different hardware configurations
  • Memory Requirements: Assessing GPU memory consumption during training and inference
  • Scalability: Testing performance maintenance as dataset size increases

For MedSAM 2, the evaluation included both bounding box-based and click-based prompting strategies to assess how interaction paradigms affect both accuracy and operational workflow efficiency [65]. The findings demonstrated that foundation models, particularly MedSAM 2, outperformed traditional models in both accuracy and computational efficiency for lung tumor segmentation tasks [65].

Computational Resource Requirements Analysis

Efficiency in Limited Data Scenarios

Clinical applications often involve rare cancers or specialized tasks where large annotated datasets are unavailable. Benchmarking reveals important performance differentials in low-data regimes. When downstream models were trained on randomly sampled cohorts of 300, 150, and 75 patients, the performance hierarchy shifted notably [12]:

  • In the largest sampled cohort (n=300), Virchow2 demonstrated superior performance in 8 tasks
  • With medium-sized cohorts (n=150), PRISM dominated by leading in 9 tasks
  • In the smallest cohort (n=75), results were more balanced with CONCH leading in 5 tasks, while PRISM and Virchow2 each led in 4 tasks

Notably, performance metrics remained relatively stable between n=75 and n=150 cohorts, suggesting that certain foundation models can achieve viable performance with surprisingly limited fine-tuning data [12]. This has significant implications for clinical deployment where annotated cases may be scarce.

Computational Trade-offs and Efficiency Rankings

Different model architectures entail distinct computational trade-offs that significantly impact their suitability for clinical deployment:

Vision vs. Vision-Language Models: While CONCH (vision-language) achieved the highest overall performance in comprehensive benchmarking, its superior performance was less pronounced in low-data scenarios and low-prevalence tasks [12]. This suggests that the additional complexity of vision-language architecture may not always justify the computational overhead, particularly for specialized clinical applications with limited data.

Scalability Considerations: The relationship between pretraining dataset size and downstream performance shows diminishing returns. While positive correlations (r = 0.29-0.74) were observed between downstream performance and pretraining dataset size across morphology, biomarker, and prognosis tasks, most were not statistically significant [12]. This indicates that data diversity and quality may outweigh sheer volume for foundation model effectiveness, an important consideration for resource-constrained development efforts.

Architectural Efficiency: In ovarian cancer subtyping, UNI achieved similar performance to H-optimus-0 at approximately a quarter of the computational cost [64]. This pattern appears across multiple studies, highlighting that the largest models do not necessarily provide the best computational efficiency for clinical deployment, even when they achieve marginally superior performance on benchmarks.

Implementation Toolkit for Clinical Deployment

Research Reagent Solutions

Table 3: Essential Research Reagents and Computational Tools for Foundation Model Deployment

Tool/Resource Function Implementation Considerations
STAMP Pipeline Open-source framework combining tiling, feature extraction, and MIL-based prediction Enables rigorous benchmarking of AI pipelines using community-shared encoders and datasets [15]
CLAM (Attention-Based MIL) Multiple instance learning with class-specific attention pooling Provides architectural flexibility and interpretability; outperformed TransMIL in endometrial cancer subtyping [15]
TransMIL Transformer-based aggregator using inter-tile self-attention Alternative MIL strategy for slide-level prediction from tile embeddings [15]
ABMIL (Attention-Based MIL) Commonly used slide classification technique with attention mechanisms Standard approach for whole slide image classification; slightly underperformed transformer-based aggregation in some benchmarks [12] [64]
Self-Supervised Learning (SSL) Pretraining method using unlabeled data Reduces annotation requirements; contrastive learning approaches (SimCLR) outperformed autoencoders in technical validation [29]
Federated Learning Privacy-preserving distributed training Enables multi-institutional collaboration while mitigating data silos [62] [3]
Workflow Integration Diagram

G Medical Images (CT/MRI/WSI) Medical Images (CT/MRI/WSI) Image Preprocessing\n(Tiling, Normalization) Image Preprocessing (Tiling, Normalization) Medical Images (CT/MRI/WSI)->Image Preprocessing\n(Tiling, Normalization) Clinical Text Data Clinical Text Data Data Harmonization\n(Multi-site, Multi-protocol) Data Harmonization (Multi-site, Multi-protocol) Clinical Text Data->Data Harmonization\n(Multi-site, Multi-protocol) Genomic Profiles Genomic Profiles Genomic Profiles->Data Harmonization\n(Multi-site, Multi-protocol) Foundation Model Selection\n(Vision, Vision-Language, Domain-Specific) Foundation Model Selection (Vision, Vision-Language, Domain-Specific) Image Preprocessing\n(Tiling, Normalization)->Foundation Model Selection\n(Vision, Vision-Language, Domain-Specific) Data Harmonization\n(Multi-site, Multi-protocol)->Foundation Model Selection\n(Vision, Vision-Language, Domain-Specific) Feature Extraction\n(Frozen Weights) Feature Extraction (Frozen Weights) Foundation Model Selection\n(Vision, Vision-Language, Domain-Specific)->Feature Extraction\n(Frozen Weights) Fine-Tuning\n(Partial/Full) Fine-Tuning (Partial/Full) Foundation Model Selection\n(Vision, Vision-Language, Domain-Specific)->Fine-Tuning\n(Partial/Full) Few-Shot Learning\n(Limited Data Scenarios) Few-Shot Learning (Limited Data Scenarios) Foundation Model Selection\n(Vision, Vision-Language, Domain-Specific)->Few-Shot Learning\n(Limited Data Scenarios) MIL Aggregation\n(CLAM, TransMIL, ABMIL) MIL Aggregation (CLAM, TransMIL, ABMIL) Feature Extraction\n(Frozen Weights)->MIL Aggregation\n(CLAM, TransMIL, ABMIL) Fine-Tuning\n(Partial/Full)->MIL Aggregation\n(CLAM, TransMIL, ABMIL) Few-Shot Learning\n(Limited Data Scenarios)->MIL Aggregation\n(CLAM, TransMIL, ABMIL) Diagnostic Classification\n(Subtyping, Grading) Diagnostic Classification (Subtyping, Grading) MIL Aggregation\n(CLAM, TransMIL, ABMIL)->Diagnostic Classification\n(Subtyping, Grading) Biomarker Prediction\n(Molecular Status) Biomarker Prediction (Molecular Status) MIL Aggregation\n(CLAM, TransMIL, ABMIL)->Biomarker Prediction\n(Molecular Status) Prognostic Stratification\n(Outcome Prediction) Prognostic Stratification (Outcome Prediction) MIL Aggregation\n(CLAM, TransMIL, ABMIL)->Prognostic Stratification\n(Outcome Prediction) Treatment Response Assessment Treatment Response Assessment MIL Aggregation\n(CLAM, TransMIL, ABMIL)->Treatment Response Assessment Hardware Limitations\n(GPU Memory, Processing Power) Hardware Limitations (GPU Memory, Processing Power) Hardware Limitations\n(GPU Memory, Processing Power)->Foundation Model Selection\n(Vision, Vision-Language, Domain-Specific) Data Privacy Requirements\n(Federated Learning Options) Data Privacy Requirements (Federated Learning Options) Data Privacy Requirements\n(Federated Learning Options)->Data Harmonization\n(Multi-site, Multi-protocol) Clinical Workflow Integration\n(Speed, Interpretability) Clinical Workflow Integration (Speed, Interpretability) Clinical Workflow Integration\n(Speed, Interpretability)->MIL Aggregation\n(CLAM, TransMIL, ABMIL)

Foundation Model Clinical Deployment Workflow

Based on comprehensive benchmarking evidence, successful clinical deployment of cancer imaging foundation models requires careful consideration of the performance-efficiency trade-off. For most clinical applications, CONCH and Virchow2 represent the current performance frontier, achieving the highest overall AUROC scores of 0.71 across diverse tasks [12]. However, for resource-constrained environments or specialized applications, models like UNI and UNI2 provide compelling alternatives, offering strong performance with significantly lower computational requirements [15] [64].

The benchmarking data consistently demonstrates that foundation models outperform conventional ImageNet-pretrained CNNs, particularly in external validation and low-data scenarios [15] [64]. This performance advantage, combined with emerging privacy-preserving technologies like federated learning, positions foundation models as the foundational technology for the next generation of computational pathology and cancer imaging tools [62] [3]. As clinical adoption accelerates, the models that will achieve widespread impact are those that balance state-of-the-art performance with practical computational efficiency, enabling integration into diverse healthcare environments without requiring specialized infrastructure.

For researchers and developers, the evidence strongly supports prioritizing foundation models over traditional CNNs for cancer imaging applications, with model selection guided by specific clinical task requirements, available computational resources, and data accessibility constraints. The ongoing development of more efficient architectures and training paradigms suggests that this balance between performance and efficiency will continue to improve, further accelerating the clinical translation of these powerful technologies.

Rigorous Validation and Comparative Analysis of State-of-the-Art Models

In precision oncology, foundation models (FMs) are transforming the analysis of medical images by learning powerful, generalizable representations from large-scale datasets. These models promise to unlock deeper insights into tumor biology, heterogeneity, and the tumor microenvironment, potentially guiding diagnosis, predicting prognosis, and monitoring therapeutic response across various cancer types [14]. However, the rapid proliferation of multiple FM architectures and pre-training strategies—including vision-only models, vision-language models, and models trained on distinct cohort sizes—has created a significant challenge for researchers: selecting the most appropriate model for specific quantitative radiomics tasks [12] [14].

Robust benchmarking frameworks are essential for rigorously evaluating these models, yet many lack systematic assessment across broad spectra of clinically relevant tasks, leaving their true capabilities and limitations incompletely understood [12]. This comprehensive guide examines current benchmarking practices for cancer imaging foundation models, compares the performance of leading models across key metrics, details essential experimental protocols, and provides visualization of critical workflows to establish methodological standards for the field.

Performance Comparison of Cancer Imaging Foundation Models

Comprehensive Benchmarking of Model Performance

Independent evaluations benchmarking multiple foundation models on external cohorts and clinically relevant tasks reveal significant performance variations across models and task types. A comprehensive assessment of 19 histopathology foundation models evaluated on 13 patient cohorts with 6,818 patients and 9,528 slides from lung, colorectal, gastric, and breast cancers provides critical insights into relative model performance [12].

Table 1: Overall Performance of Leading Foundation Models Across Cancer Types and Tasks

Foundation Model Model Type Overall Avg AUROC Morphology Avg AUROC Biomarker Avg AUROC Prognostication Avg AUROC
CONCH Vision-language 0.71 0.77 0.73 0.63
Virchow2 Vision-only 0.71 0.76 0.73 0.61
Prov-GigaPath Vision-only 0.69 - 0.72 -
DinoSSLPath Vision-only 0.69 0.76 - -
BiomedCLIP Vision-language 0.66 - - 0.61

When examining performance by cancer type, different models excelled in specific domains: CONCH achieved the highest average AUROC in stomach adenocarcinoma (STAD) and non-small-cell lung cancer (NSCLC), Virchow2 led in colorectal cancer (CRC), and BiomedCLIP performed best in breast cancer (BRCA) [12]. This suggests that optimal model selection is context-dependent, influenced by both cancer type and clinical task.

Performance Across Diagnostic and Prognostic Tasks

In radiology-focused benchmarks, performance trends vary considerably between diagnostic and prognostic tasks. The TumorImagingBench evaluation of ten medical imaging foundation models across six public datasets (3,244 scans) revealed that model performance generally decreased in prognostic tasks compared to diagnostics [14].

Table 2: Performance of Foundation Models in Diagnostic vs. Prognostic Tasks

Foundation Model LUNA16 (Diagnostic) AUC DLCS (Diagnostic) AUC NSCLC-Radiomics (Prognostic) AUC Renal Cancer (Prognostic) AUC
FMCIB 0.886 0.675 0.577 -
ModelsGenesis 0.806 0.645 0.577 0.733
VISTA3D 0.711 0.607 0.582 -
CTFM - - - 0.463

For lung nodule malignancy diagnosis, FMCIB demonstrated the highest diagnostic capability with an AUC of 0.886 on the LUNA16 dataset, while ModelsGenesis ranked second with an AUC of 0.806 [14]. In prognostic tasks, performance generally decreased, with top models achieving more modest AUCs between 0.577 and 0.733 for 2-year overall survival prediction across different cancer types [14].

Performance in Low-Data Scenarios

Model performance in data-scarce settings is particularly relevant for clinical applications involving rare molecular events or limited patient cohorts. Evaluations reveal that data diversity often outweighs data volume for foundation models, with tissue representation in pretraining datasets showing moderate correlation with performance by cancer type [12].

In benchmarking studies where downstream models were trained on randomly sampled cohorts of 300, 150, and 75 patients, Virchow2 demonstrated superior performance with 300 samples (leading in 8 tasks), while PRISM dominated in medium-sized cohorts (150 samples, leading in 9 tasks). With the smallest cohort size (75 samples), results were more balanced, with CONCH leading in 5 tasks, and PRISM and Virchow2 each leading in 4 tasks [12]. Notably, performance metrics remained relatively stable between 75 and 150 patient cohorts, suggesting potential for effective model application even in limited-data scenarios.

Experimental Protocols for Benchmarking Studies

Dataset Curation and Preprocessing

Robust benchmarking begins with meticulous dataset curation. The Mammo-Bench dataset construction exemplifies comprehensive curation, collating data from six well-curated resources to create a large-scale benchmark of 19,731 high-quality mammographic images from 6,500 patients across 6 countries [8]. Their preprocessing pipeline included breast segmentation, pectoral muscle removal, and intelligent cropping to ensure consistency while preserving clinically relevant features [8]. Similar rigorous approaches are essential for valid benchmarking across diverse patient populations and imaging protocols.

For computational pathology, benchmark evaluations typically involve tessellating whole-slide images (WSIs) into small, non-overlapping patches, followed by image feature extraction [12]. These extracted features serve as inputs for training classification or regression models tailored for specific tasks such as mutation prediction, survival analysis, disease grading, or cancer classification.

Cross-Validation Frameworks

Cross-validation (CV) is fundamental to reliable model evaluation, particularly for small-to-medium-sized datasets common in medical imaging. Proper CV implementation requires understanding of key terminology and methodologies [66]:

  • Sample: A single unit of observation or record within a dataset
  • Dataset: The total set of all samples available for training, validating, and testing ML models
  • Fold: A batch of samples as a subset of a Set, typically used in k-fold CV
  • Group: A sub-collection of samples that share at least one common characteristic (e.g., treating physician, hospital, or patient for multiple samples)

CrossValidationFlow Start Full Dataset Split1 Hold-Out Split Start->Split1 TrainSet Training Set (Dtrain) Split1->TrainSet TestSet Test Set (Dtest) Split1->TestSet Split2 K-Fold Split TrainSet->Split2 FinalModel Final Model Performance TestSet->FinalModel Fold1 Fold 1 (Validation) Split2->Fold1 Fold2 Fold 2 (Validation) Split2->Fold2 FoldK Fold K (Validation) Split2->FoldK TrainFolds K-1 Folds (Training) Fold1->TrainFolds Rotation Fold2->TrainFolds Rotation FoldK->TrainFolds Rotation ModelEval Model Evaluation TrainFolds->ModelEval ModelEval->FinalModel

Cross-Validation Workflow for Model Benchmarking

The hold-out CV approach involves initially splitting all available samples into two parts: Dtrain and Dtest. Cross-validation occurs within Dtrain, and the final chosen model is tested on the hold-out Dtest set [66]. Common train-test split ratios are 80-20 or 70-30, though for large datasets (e.g., 10 million samples), a 99:1 split may suffice if the test set adequately represents the target distribution [66].

Several CV variations exist for different scenarios:

  • K-Fold CV: Randomly splits the dataset into k distinct folds, with k-1 folds for training and one for validation
  • Leave-One-Out CV: Uses all samples except one for training, with the remaining sample for validation (valuable for small datasets but computationally expensive)
  • Leave-P-Out CV: Leaves more than one but less than n samples out for validation, with p as a hyperparameter
  • Stratified CV: Maintains the distribution of target variables in each fold
  • Grouped CV: Keeps groups of related samples together in the same fold to prevent data leakage

Recent research highlights critical considerations for CV in model comparison. Statistical tests comparing model accuracy in CV settings can be sensitive to the number of folds (K) and repetitions (M), with higher K and M combinations increasing the likelihood of detecting significant accuracy differences even between models with the same intrinsic predictive power [67]. This underscores the need for standardized, rigorous testing procedures to avoid exacerbating the reproducibility crisis in machine learning research.

Evaluation Metrics and Statistical Testing

Comprehensive benchmarking employs multiple evaluation metrics to assess different aspects of model performance:

  • Predictive Accuracy: ROC AUC (model discrimination ability across thresholds), PR AUC (precision in imbalanced datasets), F1 Score (balance between precision and recall at operational threshold) [68]
  • Fairness: Impact Ratio (ratio of scoring rates between highest and lowest scoring groups), Scoring Rate (frequency each group scores above selection threshold) [68]
  • Robustness: Test-retest reliability, input stability analysis, saliency-based interpretability [14]

For statistical comparisons, researchers must account for the inherent dependencies in CV results. The common practice of using paired t-tests on K×M accuracy scores from repeated CV is fundamentally flawed due to training fold overlaps inducing implicit dependency in accuracy scores, violating the assumption of sample independence in most hypothesis testing procedures [67].

Visualization of Benchmarking Workflows

Comprehensive Benchmarking Framework

The benchmarking process for cancer imaging foundation models follows a systematic workflow from dataset curation through model evaluation and comparison. The following diagram illustrates this comprehensive framework:

BenchmarkingFramework Start Dataset Curation Preprocessing Data Preprocessing Start->Preprocessing FeatureExtraction Feature Extraction Preprocessing->FeatureExtraction ModelAdaptation Model Adaptation FeatureExtraction->ModelAdaptation CrossValidation Cross-Validation ModelAdaptation->CrossValidation Evaluation Model Evaluation CrossValidation->Evaluation Comparison Model Comparison Evaluation->Comparison Results Benchmark Results Comparison->Results Subgraph1 Data Preparation Subgraph2 Model Processing Subgraph3 Evaluation

Cancer Imaging Foundation Model Benchmarking

Key Datasets for Benchmarking

Robust benchmarking requires diverse, well-curated datasets representing various cancer types and clinical tasks. The following table details essential datasets used in foundation model evaluation:

Table 3: Essential Medical Imaging Datasets for Benchmarking

Dataset Modality Volume Cancer Types Key Annotations
The Cancer Imaging Archive (TCIA) Multi-modal Large Various cancer types De-identified medical images of cancer [69]
Mammo-Bench Mammography 19,731 images Breast cancer BI-RADS scores, breast density, abnormality types [8]
NSCLC-Radiomics CT 3,244 scans Non-small cell lung cancer Overall survival, tumor characteristics [14]
C4KC-KiTS CT - Renal cancer 2-year overall survival, tumor segmentation [14]
Colorectal-Liver-Metastases CT/MRI - Colorectal cancer Survival data, metastasis information [14]
ADNI MRI 222 patients Alzheimer's disease Diagnosis, clinical markers [67]

Computational Tools and Frameworks

Implementation of robust benchmarking requires specific computational tools and frameworks:

  • Multiple Instance Learning (MIL): Transformer-based aggregation or attention-based MIL (ABMIL) for whole-slide image analysis [12]
  • Parameter-Efficient Fine-Tuning (PEFT): LoRA, BitFit, and other adapters for adapting foundation models with limited data [38]
  • Embedding Extraction: Methods for deriving tile-level or region-level embeddings from foundation models for downstream tasks [12] [14]
  • Statistical Testing Frameworks: Properly calibrated tests for comparing model performance that account for CV dependencies [67]

The rigorous benchmarking of cancer imaging foundation models reveals several critical insights. First, no single model demonstrates universal superiority across all cancer types and clinical tasks, though certain models like CONCH, Virchow2, FMCIB, and ModelsGenesis show consistent strong performance across multiple evaluation scenarios [12] [14]. Second, model performance is significantly influenced by task type, with diagnostic tasks generally achieving higher performance metrics than prognostic predictions [14]. Third, optimal model selection depends on specific context, including cancer type, clinical task, and available data volume [12].

The establishment of robust benchmarking frameworks requires standardized methodologies including appropriate cross-validation strategies that account for data dependencies, multiple evaluation metrics that assess both accuracy and fairness, and comprehensive reporting that enables meaningful comparison across studies. As the field advances, increased focus on model robustness, interpretability, and fairness will be essential for clinical translation. The benchmarking frameworks, metrics, and methodologies detailed in this guide provide a foundation for rigorous evaluation that can accelerate the development of more reliable, generalizable, and clinically useful foundation models for cancer imaging.

Within the rapidly advancing field of medical artificial intelligence, foundation models have emerged as powerful tools for analyzing complex clinical imaging data. For specialized domains like computational pathology and oncology, a key architectural question remains: do models that process only visual data (vision-only models) perform better, or those that jointly understand both images and text (vision-language models)? This guide provides a systematic, data-driven comparison of these two approaches, focusing on their performance when validated on external, real-world patient cohorts. Such external benchmarking is critical for assessing true generalizability and predicting real-world clinical utility, moving beyond optimistic internal validations.

Experimental Benchmarking & Performance Data

Large-Scale Histopathology Model Benchmark

A landmark 2025 study provided a comprehensive benchmark of 19 foundation models on 31 clinically relevant tasks across 6,818 patients and 9,528 whole-slide images from lung, colorectal, gastric, and breast cancers. The models were evaluated on weakly supervised tasks related to morphology, biomarkers, and prognostication [12].

Table 1: Overall Model Performance Across 31 Clinical Tasks (Mean AUROC) [12]

Model Name Model Type Morphology (5 tasks) Biomarkers (19 tasks) Prognostication (7 tasks) Overall Average
CONCH Vision-Language 0.77 0.73 0.63 0.71
Virchow2 Vision-Only 0.76 0.73 0.61 0.71
Prov-GigaPath Vision-Only - 0.72 - 0.69
DinoSSLPath Vision-Only 0.76 - - 0.69
BiomedCLIP Vision-Language - - 0.61 0.66

The data reveals that the top-performing vision-language model (CONCH) and the top-performing vision-only model (Virchow2) achieved identical overall performance (AUROC: 0.71), with each showing slight advantages in specific domains. CONCH, trained on 1.17 million image-caption pairs, matched the performance of Virchow2, which was trained on a much larger dataset of 3.1 million whole-slide images, suggesting that data diversity and architectural approach may outweigh sheer data volume [12].

Performance in Low-Data Scenarios

The benchmarking also evaluated how these models perform with limited training data, a common scenario in clinical practice for rare conditions or biomarkers. When downstream models were trained on progressively smaller patient cohorts (300, 150, and 75 patients), the results indicated that no single model type consistently dominated across all data scales [12].

Table 2: Leading Model by Number of Tasks in Low-Data Settings [12]

Sampled Cohort Size Virchow2 (Vision-Only) PRISM (Vision-Only) CONCH (Vision-Language)
300 patients 8 tasks 7 tasks -
150 patients 6 tasks 9 tasks -
75 patients 4 tasks 4 tasks 5 tasks

In the smallest cohort setting (n=75), the vision-language model CONCH led in the highest number of tasks (5), suggesting that the semantic alignment learned by vision-language models may be particularly beneficial when very limited annotated data is available for training downstream predictors [12].

Detailed Experimental Protocols

Benchmarking Framework for Histopathology Foundation Models

The comprehensive benchmarking study employed a standardized methodology to ensure fair comparison across the 19 foundation models [12]:

  • Dataset Composition: The evaluation utilized 6,818 patients and 9,528 whole-slide images from four cancer types (lung, colorectal, gastric, and breast) collected from multiple institutions across different countries. Crucially, all external cohorts were entirely separate from any foundation model's pretraining data, effectively mitigating risks of data leakage.

  • Task Formulation: Models were evaluated on 31 weakly supervised downstream prediction tasks categorized into three clinically relevant domains:

    • Morphology (5 tasks): Related to tissue structure and organization.
    • Biomarkers (19 tasks): Prediction of specific molecular biomarkers.
    • Prognostication (7 tasks): Predicting patient outcomes.

  • Feature Extraction and Aggregation: For each whole-slide image, the process involved:

    • Tiling: Dividing the whole-slide image into small, non-overlapping patches.
    • Embedding: Using each foundation model to extract feature embeddings for every patch.
    • Aggregation: Employing a multiple instance learning (MIL) framework with a transformer-based aggregator to combine patch-level embeddings into a slide-level representation for the final classification or prediction task. The study compared this approach with attention-based MIL (ABMIL), finding transformer-based aggregation slightly superior (average AUROC difference: +0.01) [12].

  • Evaluation Metrics: Primary performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUROC). Additional metrics including Area Under the Precision-Recall Curve (AUPRC), Balanced Accuracy, and F1-score were also calculated for comprehensive comparison.

Vision-Language Model for Surgical Incision Recognition

A separate study developed and evaluated a surgical incision recognition system ("DeepIncision") using a Grounded Language-Image Pre-training vision-language foundation model, demonstrating a different application of VLMs in clinical settings [70].

  • Dataset: The study utilized 1,008 surgical incision images from 865 postoperative patients for primary development, with temporal (252 images) and external (183 images from a different center) validation cohorts.

  • Task: The model was designed to recognize and classify seven distinct categories of surgical incision states: no abnormality, redness, suppuration, scab, tension blisters, ecchymosis around the incision, and dehiscence.

  • Evaluation: Performance was measured using Average Precision (AP) and Average Recall (AR). The DeepIncision system achieved an AP of 68.50% on the temporal validation cohort and 57.85% on the external validation cohort, significantly outperforming traditional deep learning object detection methods and non-expert human evaluators [70].

Visualization of Experimental Workflows

Benchmarking Workflow for Foundation Models

The following diagram illustrates the comprehensive benchmarking pipeline used to evaluate and compare vision and vision-language foundation models on external cohorts, from data preparation to performance assessment.

G cluster_1 Data Preprocessing cluster_2 Model Training & Evaluation start Start: External Cohorts (6,818 Patients, 9,528 Slides) patch Whole-Slide Image Tiling (Non-overlapping Patches) start->patch end Performance Comparison & Analysis embed Feature Embedding Extraction (Vision or Vision-Language FM) patch->embed aggregate Feature Aggregation (Transformer-based MIL) embed->aggregate predict Downstream Task Prediction (Morphology, Biomarkers, Prognosis) aggregate->predict evaluate Performance Evaluation (AUROC, AUPRC, F1-Score) predict->evaluate evaluate->end model_type Model Type Input: Vision-Only vs. Vision-Language model_type->embed Influences Embedding Quality

Foundation Model Benchmarking Pipeline

Vision-Language Model Training Paradigm

This diagram outlines the core architectural difference and training methodology for vision-language models, which leverage paired image-text data, compared to vision-only models.

G title Vision-Language Model Training image_data Medical Images (Whole-Slide Images, Scans) vision_encoder Vision Encoder image_data->vision_encoder text_data Textual Descriptions (Reports, Captions, Labels) text_encoder Language Model text_data->text_encoder fusion Multimodal Fusion Mechanism vision_encoder->fusion text_encoder->fusion joint_representation Aligned Joint Representation fusion->joint_representation vision_only Vision-Only Model (Uses only image data) vision_only->vision_encoder Lacks text alignment

VLM vs Vision-Only Training

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Resources for Foundation Model Benchmarking in Medical Imaging

Resource Type Specific Example Function & Application
Vision-Language Foundation Models CONCH [12] Extracts semantically aligned image-text features; optimal for biomarker prediction and morphological analysis.
Vision-Only Foundation Models Virchow2 [12] Provides high-quality visual representations from large-scale WSI datasets; strong overall performer.
Benchmarking Datasets Multi-center cohorts (Lung, Colorectal, Gastric, Breast cancers) [12] Provides external validation sets essential for assessing model generalizability and mitigating data leakage.
Feature Aggregation Framework Transformer-based Multiple Instance Learning (MIL) [12] Aggregates patch-level embeddings into slide-level predictions for weakly supervised WSI classification.
Evaluation Metrics Suite AUROC, AUPRC, Balanced Accuracy, F1-Score [12] Provides comprehensive assessment of model performance across different task difficulties and class imbalances.

The comparative analysis reveals a nuanced performance landscape between vision and vision-language foundation models in cancer imaging. No single model type universally dominates; instead, the optimal choice is highly task-dependent and context-specific. Vision-language models like CONCH demonstrate particular strength in tasks requiring semantic alignment between visual patterns and clinical concepts, sometimes showing an advantage in low-data scenarios. Conversely, vision-only models like Virchow2 remain powerful competitors, especially when trained on massive, diverse image datasets. Critically, the benchmarking demonstrates that models from both categories can achieve state-of-the-art performance, and that ensemble approaches combining both vision and vision-language models frequently outperform individual models by leveraging their complementary strengths. For researchers and clinicians, this underscores the importance of task-specific evaluation on external cohorts—rather than architectural preference—when selecting foundation models for clinical translation.

Assessing Generalizability and Real-World Performance Beyond Training Data

The deployment of foundation models in cancer imaging represents a paradigm shift in computational oncology. These models, pre-trained on vast datasets, promise to enhance diagnostic accuracy, predict treatment response, and improve risk stratification. However, their real-world clinical utility is critically dependent on one factor: generalizability, or the ability to maintain performance when applied to data from different institutions, patient populations, and imaging protocols than those used in training. Recent research consistently reveals that these models often suffer significant performance degradation when faced with such distribution shifts, highlighting an urgent need for systematic benchmarking of their robustness [71]. This guide provides a comparative analysis of foundation model performance across diverse real-world scenarios, detailing experimental protocols and offering a toolkit for researchers to evaluate generalizability in cancer imaging applications.

Comparative Performance Analysis of Foundation Models

Performance Across Cancer Types and Modalities

Foundation models exhibit variable performance depending on cancer type, imaging modality, and clinical task. The following table synthesizes quantitative benchmarking data from large-scale independent evaluations, providing a cross-sectional view of model generalizability.

Table 1: Benchmarking Performance of Foundation Models Across Cancer Types and Tasks

Cancer Type Imaging Modality Model Task Performance (AUC) Notes
Multiple Cancers (Lung, Colorectal, Gastric, Breast) Histopathology (WSI) CONCH Biomarker Prediction 0.71 (avg) Vision-language model; highest overall performance [12]
Virchow2 Biomarker Prediction 0.71 (avg) Vision-only model; close second to CONCH [12]
Lung CT FMCIB Nodule Malignancy (LUNA16) 0.886 Top diagnostic performer on this dataset [14]
CT ModelsGenesis Nodule Malignancy (LUNA16) 0.806 Strong consistent performance across datasets [14]
CT VISTA3D NSCLC Prognosis 0.582 Relatively stronger in prognostic tasks [14]
Renal CT ModelsGenesis 2-Year Survival (C4KC-KiTS) 0.733 Top performer for renal cancer prognosis [14]
Colorectal Liver Metastases CT FMCIB Survival Prediction 0.572 Only model substantially above random chance [14]
Performance Degradation in External Validations

A critical measure of generalizability is performance consistency when models are applied to external cohorts not seen during training. Benchmarking studies specifically designed to test this have revealed significant challenges.

Table 2: Performance Degradation in External Validation and Low-Data Scenarios

Validation Context Model/Study Focus Key Finding on Generalizability Implication
Multiple External Cohorts 19 Pathology Foundation Models CONCH and Virchow2 led, but performance varied across tasks and cohorts [12] No single model is universally superior; task-specific selection is crucial.
Clinical Setting Shift 8 Lung Nodule Prediction Models Models failed to generalize across screening, incidental, and biopsied nodule settings [71] Models trained on screening populations do not work well for incidental findings.
Low-Data Scenarios Pathology Models (CONCH) Superior performance was less pronounced in low-data and low-prevalence tasks [12] Data scarcity remains a challenge even for large foundation models.
Multimodel Ensemble CONCH + Virchow2 Ensemble outperformed individual models in 55% of tasks [12] Combining complementary models can enhance robustness.

Experimental Protocols for Assessing Generalizability

Benchmarking Framework Design

Independent evaluations of foundation models require rigorous experimental designs to generate clinically meaningful generalizability assessments. The following protocol outlines key methodological considerations:

1. Dataset Curation and Cohort Selection:

  • Multi-Cohort Validation: Incorporate multiple independent patient cohorts that were not part of any foundation model's training data. For example, one benchmarking study utilized 6,818 patients and 9,528 slides across 13 external cohorts from lung, colorectal, gastric, and breast cancers to mitigate data leakage concerns [12].
  • Diverse Clinical Endpoints: Evaluate models on clinically relevant tasks spanning morphology assessment (e.g., tumor grading), biomarker prediction (e.g., genetic mutations), and prognostic outcome prediction [12].
  • Modality Representation: Include diverse imaging modalities (CT, MRI, PET, whole-slide images) from various scanner manufacturers and acquisition protocols to test robustness to technical variations [72].

2. Model Evaluation and Statistical Analysis:

  • Performance Metrics: Utilize area under the receiver operating characteristic curve (AUROC) as a primary metric, supplemented by area under the precision-recall curve (AUPRC), balanced accuracy, and F1 scores to comprehensively assess model performance [12].
  • Statistical Comparison: Employ statistical testing (e.g., significance at P < 0.05) to compare model performance across tasks, with correction for multiple comparisons where appropriate [12].
  • Embedding Analysis: Examine the stability of model embeddings through test-retest reliability assessments and robustness to input variations, calculating metrics like cosine similarity between embeddings from slightly perturbed inputs [14].
Testing Under Distribution Shifts

A comprehensive generalizability assessment must explicitly test model performance under various distribution shifts encountered in real-world clinical practice:

1. Population Shift Testing:

  • Site-Specific Evaluation: Stratify performance by institution to identify site-specific performance degradation [71].
  • Demographic Stratification: Evaluate model performance across patient subgroups defined by age, sex, ethnicity, and socioeconomic factors to identify potential biases [73].

2. Clinical Setting Transfer:

  • Context Adaptation: Test models trained in one clinical setting (e.g., screening-detected lung nodules) on data from different settings (e.g., incidentally detected or biopsied nodules) [71].
  • Prevalence Adjustment: Assess performance in low-prevalence scenarios that mirror real-world clinical applications, particularly for rare molecular events or uncommon cancer subtypes [12].

3. Technical Robustness Evaluation:

  • Image Harmonization: Test model resilience to variations in imaging protocols, scanner types, and reconstruction algorithms through intentional image perturbations and harmonization techniques [71].
  • Annotation Stability: Evaluate sensitivity to annotation variability by measuring performance changes with different annotation seed points or segmentation boundaries [14].

The workflow below illustrates the key steps for a robust generalizability assessment.

G cluster_1 Data Preparation Phase cluster_2 Testing & Analysis Phase Start Define Generalizability Assessment Objectives Data Multi-Cohort Data Curation Start->Data Model Foundation Model Selection Data->Model Data->Model Shift Design Distribution Shift Tests Model->Shift Model->Shift Eval Execute Model Evaluation Shift->Eval Analysis Performance Analysis & Robustness Scoring Eval->Analysis Eval->Analysis Report Generate Generalizability Report Analysis->Report

Research Reagent Solutions

Table 3: Essential Resources for Foundation Model Generalizability Research

Resource Category Specific Tool / Resource Function in Generalizability Research
Public Data Repositories NCI Image Data Commons (IDC) [74] Provides curated, publicly available cancer imaging collections with AI-generated annotations for diverse populations and scanner types.
The Cancer Imaging Archive (TCIA) [74] Offers extensive, multi-institutional imaging datasets essential for external validation studies.
Benchmarking Platforms TumorImagingBench [14] Curated benchmark comprising multiple public datasets (3,244 scans) with varied oncological endpoints for standardized evaluation.
Model Architectures Vision Transformers (ViTs) [75] Transformer-based architectures effective for capturing complex morphological patterns in medical images.
nnU-Net [76] [74] Framework for automatic segmentation that adapts to dataset properties; used for generating ground truth annotations.
Robustness Assessment Tools Image Harmonization Methods [71] Techniques to mitigate scanner and protocol variations across institutions, improving model portability.
Fine-tuning Protocols [71] Methods to adapt pre-trained models to local patient populations and imaging environments.
Implementation Framework for Robustness Testing

The following workflow details the practical implementation of robustness tests based on task-specific priorities, a critical yet often overlooked aspect of generalizability assessment.

G cluster_knowledge Knowledge Tests cluster_population Population Tests cluster_uncertainty Uncertainty Tests Spec Define Robustness Specification (Priority Scenarios) K Knowledge Integrity Tests Spec->K P Population Structure Tests Spec->P U Uncertainty Awareness Tests Spec->U E Execute Priority Tests K->E K1 • Typos/Distractions • Entity Substitution K->K1 K2 • Imaging Artifacts • Morphology Changes K->K2 P->E P1 • Subgroup Analysis • Corner Case Detection P->P1 U->E U1 • Prompt Perturbation • Out-of-Context Cases U->U1 I Implement Mitigation Strategies E->I

The comprehensive benchmarking of cancer imaging foundation models reveals a consistent theme: while these models show impressive performance on internal validations, their generalizability to real-world clinical settings remains limited. The evidence indicates that no single model universally outperforms others across all cancer types, clinical contexts, and imaging modalities. Key strategies to enhance generalizability include employing model ensembles that leverage complementary strengths, implementing rigorous multi-site validation protocols, and utilizing fine-tuning approaches adapted to local populations [12] [71]. For researchers and drug development professionals, these findings underscore the importance of task-specific model selection and the necessity of extensive external validation before clinical implementation. As the field progresses, the development of standardized generalizability benchmarks and reporting standards will be crucial for translating the promise of foundation models into reliable clinical tools that benefit diverse patient populations.

The advent of foundation models is fundamentally transforming computational pathology and radiology. These models, pre-trained on massive datasets, promise to serve as versatile feature extractors for diverse downstream clinical tasks, from cancer subtyping to treatment response prediction. However, the rapid proliferation of these models has created a critical need for rigorous, large-scale benchmarking to guide researchers and clinicians in selecting optimal models for specific applications. This guide synthesizes insights from recent independent evaluations, comparing the performance of leading foundation models across key cancer imaging tasks to provide an objective, data-driven resource for the research community.

Benchmarking Histopathology Foundation Models

Performance Across Key Histopathological Tasks

Recent large-scale studies have evaluated numerous histopathology foundation models across a spectrum of clinically relevant tasks, including morphological classification, biomarker prediction, and prognostication. The following table summarizes the performance of top-performing models as reported in multi-task benchmarks.

Table 1: Performance of Leading Histopathology Foundation Models Across Multiple Tasks

Foundation Model Model Type Avg. AUROC (Morphology) Avg. AUROC (Biomarkers) Avg. AUROC (Prognosis) Key Strengths
CONCH Vision-Language 0.77 [12] 0.73 [12] 0.63 [12] Best overall performer, excels in multimodal integration
Virchow2 Vision-Only 0.76 [12] 0.73 [12] 0.61 [12] Close second overall, strong biomarker prediction
Prov-GigaPath Vision-Only - 0.72 [12] - High performance on biomarker-related tasks
DinoSSLPath Vision-Only 0.76 [12] - - Strong morphological classification
H-optimus-0 Vision-Only - - - Balanced performance across tasks [12]

Ovarian Cancer Subtyping: A Case Study in Rigorous Validation

One of the most comprehensive single-task validation studies focused on ovarian carcinoma morphological subtyping, comparing three ImageNet-pretrained encoders and fourteen foundation models trained with 1,864 whole slide images (WSIs). The best-performing classifier utilized the H-optimus-0 foundation model, achieving balanced accuracies of 89% on hold-out testing, 97% on the Transcanadian external validation set, and 74% on the highly heterogeneous OCEAN Challenge set [64]. The UNI model achieved comparable results at approximately a quarter of the computational cost, highlighting the importance of considering efficiency alongside pure performance metrics [64].

This study emphasized that hyperparameter tuning improved performance by a median of 1.9% balanced accuracy, with many improvements being statistically significant. This finding underscores that reported performance differences between models may reflect suboptimal implementation rather than inherent capability [64].

Experimental Protocol for Histopathology Benchmarking

The methodological framework for benchmarking histopathology foundation models typically follows a standardized pipeline:

Table 2: Key Research Reagent Solutions in Histopathology Foundation Model Evaluation

Research Reagent Function in Evaluation Examples/Specifications
Whole Slide Images (WSIs) Primary input data for model evaluation Formalin-fixed, paraffin-embedded (FFPE) tissue; 40× magnification; H&E staining [64]
Multiple Instance Learning (MIL) Frameworks Handles gigapixel-scale WSI analysis Attention-based MIL (ABMIL) [64], Transformer-based MIL [12], MI-SimpleShot [77]
Feature Extractors Convert image patches to feature vectors Histopathology foundation models (e.g., UNI, Virchow2, CONCH) [77] or ImageNet-pretrained encoders [64]
External Validation Sets Tests model generalizability Transcanadian Study (80 WSIs) [64], OCEAN Challenge (513 WSIs) [64], AI4SkIN dataset [77]
Statistical Metrics Quantifies model performance and significance Balanced Accuracy, AUROC, AUPRC, F1 scores, p-values for risk stratification [78]

G WSI Whole Slide Image (WSI) Patching Tissue Segmentation & Patching WSI->Patching FeatureExtraction Patch Feature Extraction (Foundation Model) Patching->FeatureExtraction FeatureMatrix Feature Matrix FeatureExtraction->FeatureMatrix MIL Multiple Instance Learning (Aggregation & Classification) FeatureMatrix->MIL Prediction Slide-Level Prediction MIL->Prediction

Figure 1: Standard Workflow for Histopathology WSI Analysis

Treatment Response Prediction: Expanding to Secondary Tasks

Beyond primary diagnostic tasks, foundation models show promise for predicting treatment response. In ovarian cancer bevacizumab response prediction, models achieved patient-level balanced accuracy close to 70% and successfully stratified high- and low-risk patients (p < 0.05) [78]. This demonstrates that features learned from large-scale histopathology datasets transfer effectively to specialized predictive tasks where no established biomarkers exist.

Benchmarking Radiology Foundation Models

Performance in Radiology Report Generation

In radiology, foundation models have shown remarkable progress in automated report generation. Advanced models like MedVersa achieved RadCliQ-v1 scores of 1.46 ± 0.03 on IU X-ray findings sections, outperforming other AI systems in clinical relevance metrics [79]. Comparative studies reveal that AI models can surpass radiologists in certain diagnostic tasks, with higher AUROC scores (0.91 vs. 0.86) and detection of 6.8% more clinically significant findings [79].

Quality assessments of AI-generated reports reflect significant improvements: radiologists rated summary quality at 4.86/5 and recommendation agreement at 4.94/5, while patient comprehension scores nearly doubled (from 2.71 to 4.69/5) when layperson-friendly AI reports were used [79].

Experimental Protocol for Radiology Benchmarking

The evaluation of radiology foundation models typically employs distinct methodologies tailored to medical imaging data:

Table 3: Essential Research Reagent Solutions for Radiology Foundation Model Assessment

Research Reagent Function in Evaluation Examples/Specifications
Multi-modal Medical Images Primary input data for model training and testing Chest X-rays, CT scans, MRI scans from diverse patient populations [79]
Vision-Language Models (VLMs) Generate reports from images MedVersa, Transformer-based encoder-decoders, CLIP-integrated models [79]
Text Summarization Models Condense existing radiology reports T5 models, BERT-based architectures [79]
Evaluation Metrics Assess clinical utility and accuracy RadCliQ-v1, BERTScore, F1 scores, clinical relevance ratings [79]
Human Evaluation Frameworks Validate clinical applicability Radiologist ratings (quality, agreement), patient comprehension scores [79]

G MedicalImage Medical Image (X-ray, CT, MRI) ImageEncoder Image Encoder (Vision Transformer, CNN) MedicalImage->ImageEncoder ImageFeatures Image Feature Representation ImageEncoder->ImageFeatures TextDecoder Text Decoder (Transformer, RNN) ImageFeatures->TextDecoder GeneratedReport Generated Radiology Report TextDecoder->GeneratedReport HumanEvaluation Clinical Validation (Radiologist Assessment) GeneratedReport->HumanEvaluation

Figure 2: Radiology Report Generation Pipeline

Key Findings from Comparative Analyses

Several consistent patterns emerge from independent benchmarking studies across histopathology and radiology:

  • Data Diversity Over Volume: While dataset scale matters, diversity of tissue types, imaging protocols, and clinical sources appears more critical for model generalizability [12] [77].

  • Complementary Model Strengths: Different foundation models trained on distinct cohorts learn complementary features. Ensemble approaches combining top models like CONCH and Virchow2 outperform individual models in approximately 55% of tasks [12].

  • Vision-Language Fusion: Multimodal models demonstrate particular strength in tasks requiring joint understanding of visual patterns and clinical context, though their advantage diminishes in low-data scenarios [12].

  • Generalization Challenges: Models exhibit performance degradation across distribution shifts, such as different scanner types or hospital protocols. The proposed Foundation Model-Silhouette Index (FM-SI) metric helps quantify this sensitivity [77].

Methodological Considerations for Robust Benchmarking

  • Hyperparameter Sensitivity: Performance differences between models can be substantially influenced by optimization efforts, with one study reporting median improvements of 1.9% balanced accuracy through systematic tuning [64].

  • Aggregation Strategy Impact: In histopathology, the choice of MIL framework (attention-based vs. similarity-based) interacts with feature extractor capabilities, with some foundation models performing better with specific aggregation strategies [77].

  • Computational Efficiency: Beyond pure accuracy, considerations like embedding dimension and parameter count affect practical deployment. For example, UNI achieved performance comparable to larger models at a quarter of the computational cost [64].

Independent large-scale benchmarking reveals that while no single foundation model dominates all tasks, consistent patterns emerge to guide model selection. In histopathology, CONCH and Virchow2 currently demonstrate leading overall performance, while in radiology, specialized vision-language models like MedVersa show impressive clinical utility. The most robust solutions often emerge from ensembles that leverage complementary strengths of multiple foundation models. As the field evolves, standardized evaluation protocols and metrics like FM-SI will be crucial for assessing model generalizability across diverse clinical environments. Future developments will likely focus on enhancing model efficiency, interpretability, and integration across imaging modalities to advance precision oncology.

Conclusion

The benchmarking of cancer imaging foundation models marks a significant advancement toward data-driven, personalized oncology. Key takeaways confirm that these models, particularly vision-language architectures, demonstrate superior performance and data efficiency compared to traditional approaches, especially in limited-data scenarios. However, no single model or fine-tuning strategy is universally optimal; the choice depends on specific data constraints and clinical tasks. Success hinges on rigorous, multi-dimensional validation that addresses data quality, fairness, and generalizability to ensure robustness in real-world clinical environments. Future efforts must focus on standardizing benchmarking protocols, fostering the development of open-source models and frameworks, and deepening the integration of multimodal data to fully realize the potential of foundation models in accelerating cancer research and improving patient care.

References