# Current Pathology Foundation Models are unrobust to Medical Center Differences

arXiv: https://arxiv.org/abs/2501.18055 · HTML: https://mlsanity.com/paper/2501.18055/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2025.

## Built on by (most cited first, 30 of 39)

- [A Survey of Pathology Foundation Model: Progress and Future Directions](https://mlsanity.com/paper/2504.04045/) (2025)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [Quantized-Tinyllava: a new multimodal foundation model enables efficient split learning](https://mlsanity.com/paper/2511.23402/) (2025)
- [MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification](https://mlsanity.com/paper/2506.17140/) (2025)
- [Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss](https://mlsanity.com/paper/2507.22092/) (2025)
- [Atlas 2 -- Foundation models for clinical deployment](https://mlsanity.com/paper/2601.05148/) (2026)
- [HASD: Hierarchical Adaption for pathology Slide-level Domain-shift](https://mlsanity.com/paper/2506.23673/) (2025)
- [Beyond the Failures: Rethinking Foundation Models in Pathology](https://mlsanity.com/paper/2510.23807/) (2025)
- [Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping](https://mlsanity.com/paper/2506.18668/) (2025)
- [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)
- [G2L:From Giga-Scale to Cancer-Specific Large-Scale Pathology Foundation Models via Knowledge Distillation](https://mlsanity.com/paper/2510.11176/) (2025)
- [Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness](https://mlsanity.com/paper/2603.05884/) (2026)
- [MVHybrid: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Models](https://mlsanity.com/paper/2508.00383/) (2025)
- [AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics](https://mlsanity.com/paper/2508.04955/) (2025)
- [A protocol for evaluating robustness to H&E staining variation in computational pathology models](https://mlsanity.com/paper/2603.12886/) (2026)
- [Auditing Data Leakage in Whole-Slide Image Multimodal Benchmarks](https://mlsanity.com/paper/2607.12278/) (2026)
- [Open-source framework for detecting bias and overfitting for large pathology images](https://mlsanity.com/paper/2503.01827/) (2025)
- [AdaFusion: Prompt-Guided Inference with Adaptive Fusion of Pathology Foundation Models](https://mlsanity.com/paper/2508.05084/) (2025)
- [From Explainable to Explained AI: Ideas for Falsifying and Quantifying Explanations](https://mlsanity.com/paper/2508.09205/) (2025)
- [Comparing Computational Pathology Foundation Models using Representational Similarity Analysis](https://mlsanity.com/paper/2509.15482/) (2025)
- [Zero-shot segmentation of skin tumors in whole-slide images with vision-language foundation models](https://mlsanity.com/paper/2511.18978/) (2025)
- [Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization](https://mlsanity.com/paper/2512.06006/) (2025)
- [DA-SSL: self-supervised domain adaptor to leverage foundational models in turbt histopathology slides](https://mlsanity.com/paper/2512.13600/) (2025)
- [PANDA-PLUS-Bench: A Clinical Benchmark for Evaluating Robustness of AI Foundation Models in Prostate Cancer Diagnosis](https://mlsanity.com/paper/2512.14922/) (2025)
- [Do Pathology Foundation Models Encode Disease Progression? A Pseudotime Analysis of Visual Representations](https://mlsanity.com/paper/2601.21334/) (2026)
- [Histopathology Image Normalization via Latent Manifold Compaction](https://mlsanity.com/paper/2602.24251/) (2026)
- [When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework](https://mlsanity.com/paper/2605.31504/) (2026)
- [Vision-Language Models as Zero-Annotation Oracles in Histopathology](https://mlsanity.com/paper/2606.16658/) (2026)
- [Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis](https://mlsanity.com/paper/2606.19966/) (2026)
- [Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation](https://mlsanity.com/paper/2606.28697/) (2026)

## Abstract (arXiv)

Pathology Foundation Models (FMs) hold great promise for healthcare. Before they can be used in clinical practice, it is essential to ensure they are robust to variations between medical centers. We measure whether pathology FMs focus on biological features like tissue and cancer type, or on the well known confounding medical center signatures introduced by staining procedure and other differences. We introduce the Robustness Index. This novel robustness metric reflects to what degree biological features dominate confounding features. Ten current publicly available pathology FMs are evaluated. We find that all current pathology foundation models evaluated represent the medical center to a strong degree. Significant differences in the robustness index are observed. Only one model so far has a robustness index greater than one, meaning biological features dominate confounding features, but only slightly. A quantitative approach to measure the influence of medical center differences on FM-based prediction performance is described. We analyze the impact of unrobustness on classification performance of downstream models, and find that cancer-type classification errors are not random, but specifically attributable to same-center confounders: images of other classes from the same medical center. We visualize FM embedding spaces, and find these are more strongly organized by medical centers than by biological factors. As a consequence, the medical center of origin is predicted more accurately than the tissue source and cancer type. The robustness index introduced here is provided with the aim of advancing progress towards clinical adoption of robust and reliable pathology FMs.