# A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models

arXiv: https://arxiv.org/abs/2407.06508 · HTML: https://mlsanity.com/paper/2407.06508/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [LongNet: Scaling Transformers to 1,000,000,000 Tokens](https://mlsanity.com/paper/2307.02486/) (2023)
- [Benchmarking Self-Supervised Learning on Diverse Pathology Datasets](https://mlsanity.com/paper/2212.04690/) (2022)
- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
- [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://mlsanity.com/paper/2202.08360/) (2022)
- [A General-Purpose Self-Supervised Model for Computational Pathology](https://mlsanity.com/paper/2308.15474/) (2023)
- [RudolfV: A Foundation Model by Pathologists for Pathologists](https://mlsanity.com/paper/2401.04079/) (2024)
- [SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving](https://mlsanity.com/paper/2206.14116/) (2022)
- [Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images](https://mlsanity.com/paper/2310.07033/) (2023)
- [Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image](https://mlsanity.com/paper/2106.12265/) (2021)
- [Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling](https://mlsanity.com/paper/2206.08885/) (2022)
- [H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma](https://mlsanity.com/paper/2206.10573/) (2022)

## Built on by (most cited first, 30 of 66)

- [Multimodal Whole Slide Foundation Model for Pathology](https://mlsanity.com/paper/2411.19666/) (2024)
- [Phikon-v2, A large and public feature extractor for biomarker prediction](https://mlsanity.com/paper/2409.09173/) (2024)
- [Do Multiple Instance Learning Models Transfer?](https://mlsanity.com/paper/2506.09022/) (2025)
- [PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology](https://mlsanity.com/paper/2505.20202/) (2025)
- [Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study](https://mlsanity.com/paper/2411.05489/) (2024)
- [A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks](https://mlsanity.com/paper/2501.15724/) (2025)
- [A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models](https://mlsanity.com/paper/2408.14496/) (2024)
- [Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts](https://mlsanity.com/paper/2410.06723/) (2024)
- [Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models](https://mlsanity.com/paper/2601.04163/) (2026)
- [How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark](https://mlsanity.com/paper/2503.12990/) (2025)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics](https://mlsanity.com/paper/2508.01490/) (2025)
- [Pathology Foundation Models](https://mlsanity.com/paper/2407.21317/) (2024)
- [Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation](https://mlsanity.com/paper/2506.21444/) (2025)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [Beyond Diagnostic Performance: Revealing and Quantifying Ethical Risks in Pathology Foundation Models](https://mlsanity.com/paper/2502.16889/) (2025)
- [Ensemble learning of pathology foundation models for precision oncology](https://mlsanity.com/paper/2508.16085/) (2025)
- [Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning](https://mlsanity.com/paper/2603.22198/) (2026)
- [Benchmarking Domain Generalization Algorithms in Computational Pathology](https://mlsanity.com/paper/2409.17063/) (2024)
- [Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language](https://mlsanity.com/paper/2603.02790/) (2026)
- [ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology](https://mlsanity.com/paper/2503.17564/) (2025)
- [Benchmarking Foundation Models for Mitotic Figure Classification](https://mlsanity.com/paper/2508.04441/) (2025)
- [GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights](https://mlsanity.com/paper/2603.02926/) (2026)
- [Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology](https://mlsanity.com/paper/2603.08328/) (2026)
- [Unifying Multiple Foundation Models for Advanced Computational Pathology](https://mlsanity.com/paper/2503.00736/) (2025)
- [Whole Slide Concepts: A Supervised Foundation Model For Pathological Images](https://mlsanity.com/paper/2507.05742/) (2025)
- [MVHybrid: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Models](https://mlsanity.com/paper/2508.00383/) (2025)
- [MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Image](https://mlsanity.com/paper/2512.17726/) (2025)
- [A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images](https://mlsanity.com/paper/2603.15967/) (2026)
- [CytoSyn: a Foundation Diffusion Model for Histopathology -- Tech Report](https://mlsanity.com/paper/2603.18089/) (2026)

## Abstract (arXiv)

The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This will significantly enhance scientific research in computational pathology and help bridge the gap between research and clinical deployment. With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the performance of such models on a variety of clinically relevant tasks spanning multiple organs and diseases. In this work, we present a collection of pathology datasets comprising clinical slides associated with clinically relevant endpoints including cancer diagnoses and a variety of biomarkers generated during standard hospital operation from two medical centers. We leverage these datasets to systematically assess the performance of public pathology foundation models and provide insights into best practices for training new foundation models and selecting appropriate pretrained models.