Quantifying and Mitigating Hospital Domain Bias in Pathology Foundation Models using Adversarial Feature Disentanglement

Mengliang Zhang

Keywords: pathology image, foundation model, domain bias

Abstract: Pathology foundation models (PFMs) have demonstrated remarkable potential in whole-slide image (WSI) diagnosis. However, pathology images from different hospitals exhibit domain shifts due to variations in scanning hardware and preprocessing. These differences cause PFMs to learn spurious hospital-specific features, severely compromising their robustness and generalizability in clinical settings. We present the first systematic study of this hospital-source domain bias in PFMs. To address the critical trade-off between diagnostic utility and domain predictability, we establish a quantification pipeline and introduce the Robustness Index (RI). Furthermore, we propose a lightweight adversarial framework for feature disentanglement. This framework employs a trainable adapter and a domain classifier connected via a Gradient Reversal Layer (GRL) to remove latent hospital-specific information from frozen PFM representations without modifying the encoder itself. Experiments on multi-center histopathology datasets demonstrate that our approach substantially suppresses domain predictability and achieves significant gains in feature robustness. Crucially, the method maintains or improves disease classification performance, proving its efficacy particularly in out-of-domain scenarios. Our code is provided at: [https://github.com/MengRes/pfm_domain_bias](https://github.com/MengRes/pfm_domain_bias)

Primary Subject Area: Fairness and Bias

Secondary Subject Area: Application: Histopathology

Registration Requirement: Yes

Reproducibility: https://github.com/MengRes/pfm_domain_bias

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Midl Latex Submission Checklist: Ensure no LaTeX errors during compilation., Replace NNN with your OpenReview submission ID., Includes \documentclass{midl}, \jmlryear{2026}, \jmlrworkshop, \jmlrvolume, \editors, and correct \bibliography command., Did not override options of the hyperref package., Did not use the times package., Use the correct spelling and format, avoid Unicode characters, and use LaTeX equivalents instead., Any math in the title and abstract must be enclosed within $...$., Did not override the bibliography style defined in midl.cls and did not use \begin{thebibliography} directly to insert references., Avoid using \scalebox; use \resizebox when needed., Included all necessary figures and removed *unused* files in the zip archive., Removed special formatting, visual annotations, and highlights used during rebuttal., All special characters in the paper and .bib file use LaTeX commands (e.g., \'e for é)., No separate supplementary PDF uploads., Acknowledgements, references, and appendix must start after the main content.

Submission Number: 366