<p>Patch-level phenotype identification via weakly supervised single-neuron selection in sparse autoencoders for CLIP-derived pathology embeddings</p>

Keita Tamura; Yao-zhong Zhang; Yohei Okubo; Seiya Imoto

doi:10.7490/f1000research.1120390.1

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Tamura K, Zhang Yz, Okubo Y and Imoto S. Patch-level phenotype identification via weakly supervised single-neuron selection in sparse autoencoders for CLIP-derived pathology embeddings [version 1; not peer reviewed]. F1000Research 2025, 14:1242 (poster) (https://doi.org/10.7490/f1000research.1120390.1)

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Patch-level phenotype identification via weakly supervised single-neuron selection in sparse autoencoders for CLIP-derived pathology embeddings

Keita Tamura, Yao-zhong Zhang¹, Yohei Okubo, Seiya Imoto¹

Published 13 Nov 2025 (https://doi.org/10.7490/f1000research.1120390.1)

Author Affiliations

¹ Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato, Tokyo, Japan

Copyright © 2025 Tamura K et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 13 Nov 2025

Patch-level phenotype identification via weakly supervised single-neuron selection in sparse autoencoders for CLIP-derived pathology embeddings
[version 1; not peer reviewed]

Keita Tamura, Yao-zhong Zhang¹, Yohei Okubo, Seiya Imoto¹

Author Affiliations

¹ Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato, Tokyo, Japan

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Whole-slide image, Sparse autoencoder, Explainable AI

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