Histoscope: Expert-Grounded Inspection of Sparse Autoencoder Features in Histopathology Foundation Models

Computational pathology lacks open tools for inspecting sparse autoencoder (SAE) features, and few studies test whether such features align with expert judgement. We present Histoscope, an open-source tool for inspecting SAE features derived from histopathology foundation-model embeddings, and evaluate it through a blinded two-rater pathologist study. Using UNI embeddings from the SPIDER colorectal dataset, we train a TopK SAE and use Histoscope to rank features by diagnostic-class selectivity, measured with per-class AUPRC. Pathologists judged 82\% of feature panels monosemantic overall; among the 50 features Histoscope labelled monosemantic, all 50 were confirmed, while 32 of 38 features it labelled polysemantic were also judged monosemantic, mostly because they captured morphology spanning multiple diagnostic classes. Inspection tools of this kind can help make pathology foundation models more auditable by domain experts by connecting internal features to morphological evidence rather than treating embeddings as opaque vectors. We will release Histoscope, the trained SAE, and the evaluation protocol to support expert-grounded SAE inspection in computational pathology.