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StaDis: Stability distance to detecting out-of-distribution data in computational pathology.

Di Zhang1, Jiusong Ge1, Jiashuai Liu1

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.

Medical Image Analysis
|August 31, 2025
PubMed
Summary

Computational pathology models need Out-of-Distribution (OOD) detection for reliable clinical use. We introduce Stability Distance (StaDis), a novel, plug-and-play OOD method for computational pathology, achieving state-of-the-art results on new benchmarks.

Keywords:
Anomaly detectionComputational pathologyMultiple instance learningOut of distribution detectionRare case mining

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Area of Science:

  • Computational pathology
  • Artificial intelligence in medicine
  • Medical image analysis

Background:

  • Computational pathology (CPath) models enhance pathologist efficiency but risk unreliability with unseen data.
  • Lack of Out-of-Distribution (OOD) detection in CPath models hinders clinical trust and safety.
  • Existing OOD methods are not tailored for the unique challenges of computational pathology.

Purpose of the Study:

  • To introduce a novel OOD detection approach specifically designed for computational pathology.
  • To develop a method that ensures the reliability of CPath models in real-world clinical settings.
  • To establish new benchmarks for evaluating OOD detection in pathological data.

Main Methods:

  • Proposed Stability Distance (StaDis), a plug-and-play OOD detection module measuring feature discrepancies between images and their perturbed versions.
  • Explored OOD detection at the whole-slide image (WSI) level using the multiple instance learning (MIL) framework.
  • Developed pathological OOD detection benchmarks for anomaly detection, rare case mining, and frozen section identification.

Main Results:

  • StaDis achieved state-of-the-art performance in 23 out of 38 experiments and ranked second in 10.
  • Demonstrated significant improvements, with AUROC increasing by 7.91% for patch-based anomaly detection using StaDis with the 'Conch' backbone.
  • The approach proved effective across diverse pathological OOD detection scenarios.

Conclusions:

  • Stability Distance (StaDis) offers a robust and adaptable solution for OOD detection in computational pathology.
  • The proposed method enhances the reliability and trustworthiness of CPath models for clinical deployment.
  • The developed benchmarks facilitate future research and validation of OOD detection techniques in pathology.