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Related Experiment Video

Updated: Jun 13, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Towards robust foundation models for digital pathology.

Jonah Kömen1,2, Edwin D de Jong3, Julius Hense1,2

  • 1Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany.

Nature Communications
|June 11, 2026
PubMed
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This summary is machine-generated.

Biomedical Foundation Models (FMs) show robustness deficits to non-biological features, risking clinical AI adoption. PathoROB benchmark reveals these issues, highlighting the need for robust FMs in healthcare.

Area of Science:

  • Biomedical AI
  • Computational Pathology
  • Machine Learning in Healthcare

Background:

  • Biomedical Foundation Models (FMs) are increasingly used in healthcare research and clinical validation.
  • These models may learn non-biological features (e.g., lab procedures, scanner variations), posing risks for reliable clinical deployment.

Purpose of the Study:

  • To introduce PathoROB, a public benchmark for quantifying the robustness of FMs to non-biological features in pathology.
  • To assess both representation-level and output-level robustness of FMs in clinically relevant tasks.

Main Methods:

  • Developed PathoROB benchmark for evaluating FM robustness against non-biological variations.
  • Assessed representation-level robustness using a robustness index.
  • Evaluated output-level robustness across patch/slide prediction, case retrieval, and clustering tasks for 20 FMs.

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Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues
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Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues

Published on: May 1, 2019

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Last Updated: Jun 13, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues
07:40

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues

Published on: May 1, 2019

Main Results:

  • All 20 evaluated FMs exhibited robustness deficits to non-biological features.
  • Significant performance differences were observed among the evaluated FMs.
  • Non-robust FM representations were found to cause major downstream diagnostic errors.

Conclusions:

  • Robustness evaluation is critical for validating pathology FMs before clinical deployment.
  • While robust FMs, vision-language alignment, and post-hoc methods can mitigate risks, they do not eliminate them entirely.
  • PathoROB provides a framework for assessing and enhancing FM robustness in biomedical applications.