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Updated: Jan 9, 2026

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Self-supervised stain normalization empowers privacy-preserving and model generalization in digital pathology.

Jianhang Wang1, Jiahui Yu1,2, Haixu Yang1

  • 1Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang Key Laboratory of Intelligent Sensing Technology and Advanced Medical Instrument, Zhejiang University, Hangzhou, Zhejiang, China.

NPJ Digital Medicine
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces StainLUT, a self-supervised model for stain normalization in digital pathology. It enables cross-center artificial intelligence-driven digital pathology model development without data sharing, preserving privacy.

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Area of Science:

  • Digital pathology
  • Artificial intelligence in medicine
  • Computational pathology

Background:

  • Digital pathology images show color variations due to staining and scanning differences across hospitals.
  • Data integration is crucial for robust artificial intelligence-driven digital pathology (AIDP) models, but privacy concerns impede data sharing.

Purpose of the Study:

  • To develop a privacy-preserving method for stain normalization in digital pathology.
  • To enable the development of generalizable AIDP models across different institutions without direct data transfer.

Main Methods:

  • A self-supervised model named stain lookup table (StainLUT) was proposed.
  • StainLUT leverages structural similarity in pathology images for stain normalization.
  • The model was applied to single-center AIDP models for cross-center validation.

Main Results:

  • StainLUT achieved comparable performance to centralized or same-center trained AIDP models.
  • Successful cross-center tumor localization at the whole-slide level was demonstrated.
  • Accurate cross-center tumor classification at the patch level was achieved.

Conclusions:

  • StainLUT offers a privacy-preserving solution for stain normalization across unseen medical centers.
  • This approach facilitates the deployment of AIDP foundational models under strict privacy regulations.
  • The method enhances the generalizability of AIDP models by addressing inter-center image variations.