Towards Robust Foundation Models for Digital Pathology

2025-07-22

AI Summary

Pathological foundation models are vulnerable to non-biological technical variations—such as scanner heterogeneity and slide preparation protocols—undermining their reliability in clinical deployment. To address this, we present the first systematic evaluation of their technical robustness, introducing PathoROB: a standardized benchmark comprising four diverse datasets, multi-center, multi-class configurations, and three novel quantitative metrics—including a Robustness Index. Using PathoROB, we evaluate 20 state-of-the-art models and uncover pervasive robustness deficiencies across architectures and training paradigms. Furthermore, we demonstrate via post-hoc robustification that enhancing technical robustness significantly reduces downstream diagnostic error rates. This work establishes the first standardized, quantitatively grounded evaluation framework for assessing and validating the clinical readiness of pathological AI systems.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Biomedical Foundation Models (FMs) are rapidly transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biological technical features -- including variations in surgical/endoscopic techniques, laboratory procedures, and scanner hardware -- poses risks for clinical deployment. We present the first systematic investigation of pathology FM robustness to non-biological features. Our work (i) introduces measures to quantify FM robustness, (ii) demonstrates the consequences of limited robustness, and (iii) proposes a framework for FM robustification to mitigate these issues. Specifically, we developed PathoROB, a robustness benchmark with three novel metrics, including the robustness index, and four datasets covering 28 biological classes from 34 medical centers. Our experiments reveal robustness deficits across all 20 evaluated FMs, and substantial robustness differences between them. We found that non-robust FM representations can cause major diagnostic downstream errors and clinical blunders that prevent safe clinical adoption. Using more robust FMs and post-hoc robustification considerably reduced (but did not yet eliminate) the risk of such errors. This work establishes that robustness evaluation is essential for validating pathology FMs before clinical adoption and demonstrates that future FM development must integrate robustness as a core design principle. PathoROB provides a blueprint for assessing robustness across biomedical domains, guiding FM improvement efforts towards more robust, representative, and clinically deployable AI systems that prioritize biological information over technical artifacts.

Problem

• Investigates robustness of pathology foundation models to non-biological technical features
• Quantifies and mitigates risks of diagnostic errors from non-robust AI representations
• Proposes framework and benchmark for clinically deployable robust pathology AI systems

Innovation

• Introduces PathoROB robustness benchmark
• Proposes FM robustification framework
• Develops three novel robustness metrics