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Updated: Jul 17, 2026

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

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

Published on: July 11, 2025

Adapting pathology foundation models for continual cross-center WSI retrieval.

Xinyu Zhu¹, Zhiguo Jiang¹, Kun Wu¹

¹Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, 100191, China; Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China; Tianmushan Laboratory, Hangzhou, 311115, China.

Medical Image Analysis

|July 15, 2026

Summary

This study introduces a new continual learning framework to adapt foundation models for cross-center whole slide image retrieval. The method effectively addresses domain shifts, improving retrieval accuracy in multi-center medical imaging.

Keywords:

CBHIR Continual learning Digital pathology Histopathology image analysis

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Area of Science:

Computational pathology
Digital pathology
Machine learning in healthcare

Background:

Medical centers generate vast whole slide images (WSIs) for histopathological analysis.
Content-based histopathological image retrieval (CBHIR) leverages digital WSIs for morphologic content analysis.
Foundation models show promise for CBHIR but face challenges like cross-center domain shifts and feature drift.

Purpose of the Study:

To develop a continual learning framework for adapting pathology foundation models for continual cross-center WSI retrieval (CCBHIR).
To mitigate domain discrepancies and enable stable, efficient feature rehearsal for long-term model adaptation.

Main Methods:

A novel continual learning framework aligning foundation model outputs into a unified latent domain using instance-wise prompts.
An embedding consistency replay mechanism for stable feature rehearsal without index rebuilding.
Evaluation on a large-scale dataset of 10,837 WSIs from TCGA projects.

Main Results:

The proposed framework significantly improves both intra-center and cross-center retrieval performance.
Demonstrated superior performance compared to state-of-the-art continual learning methods.
Preserved forward and backward retrieval compatibility across different medical centers.

Conclusions:

The framework offers an effective strategy for deploying pathology foundation models in real-world, multi-center scenarios.
Bridges the gap between foundation model research and practical computational pathology applications.
Facilitates robust and adaptable WSI retrieval in diverse clinical settings.