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CLEAR-WSI: Towards Foundation-Model-Empowered Diagnosis Aligned Whole Slide Image Retrieval

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Peter Schüffler

Prof. Dr.

Core PI

Computational Pathology

Abstract

The rapid growth of digital pathology has produced vast repositories of hematoxylin and eosin (H&E) stained whole slide images. However, many of these archives lack structured indexing and reliable metadata, making systematic organization and retrieval challenging. Reverse image search, also known as content-based image retrieval, addresses this limitation by retrieving slides based on learned visual representations rather than incomplete or inconsistent metadata. Although retrieval systems have been deployed in digital pathology, many rely on manually engineered indexing strategies or heuristic filtering rules, which limit scalability and generalization across diverse diagnostic categories. Thus, we propose CLEAR-WSI (Constant Length Embedding & Automatic Retrieval), a fully automated slide-level retrieval framework. CLEAR-WSI learns semantically structured whole slide embeddings through an Attention-based Multiple Instance Learning framework, compressing each WSI into a fixed dimensional representation for efficient storage and scalable similarity search. Furthermore, we introduce a self-reviewing diagnostic filtering mechanism that enforces label consistency among retrieved candidates, improving diagnosis alignment without relying on manually defined class-specific rules. Across two public datasets, CAMELYON16 (lymph node metastases) and BRACS (breast cancer subtypes), our diagnostic-aware method establishes new state-of-the-art results, improving AccMV @5 from 77.49% to 89.92% on CAMELYON16, from 54.12% to 75.86% on BRACS level-1, and from 36.47% to 51.72% on BRACS level-2.

inproceedings LWL+26a

COMPAYL @MICCAI 2026

Workshop on Computational Pathology with Multimodal Data at the 29th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France, Sep 27-Oct 01, 2026. To be published. Preprint available.

Authors

J. Liu • Y. Wally • H. Li • W. Zhou • J. Dai • E. Wetzer • P. J. Schüffler

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Research Area

 C1 | Medicine

BibTeXKey: LWL+26a

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