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

Abstract excerpt

Computer-aided analysis of whole slide images (WSIs) has advanced rapidly with the emergence of multi-modal pathology foundation models. In this study, we propose a weakly supervised neuron selection approach to extract disentangled representations from CLIPderived pathology foundation models, leveraging the interpretability of sparse autoencoders. Specifically, neurons are ordered and selected using whole-slide...

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