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Dasdelen, M.F. ; Lim, H. ; Buck, M.* ; Götze, K.S.* ; Marr, C. ; Schneider, S.

CytoSAE: Interpretable Cell Embeddings for Hematology.

In: (28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, 23-27 September 2025, Daejeon). Berlin [u.a.]: Springer, 2026. 77-86 (Lect. Notes Comput. Sc. ; 15973 LNCS)
DOI
Sparse autoencoders (SAEs) emerged as a promising tool for mechanistic interpretability of transformer-based foundation models. Recently, SAEs were also adopted for the visual domain, enabling the discovery of visual concepts and their patch-wise attribution to input images. While foundation models are increasingly applied to medical imaging, tools for interpreting their predictions remain limited. In this work, we propose CytoSAE, a sparse autoencoder trained on over 40,000 peripheral blood single-cell images. CytoSAE generalizes well to diverse and out-of-domain datasets, including bone marrow cytology. Here, it identifies morphologically relevant concepts which we validated with medical experts. Furthermore, we demonstrate scenarios in which CytoSAE can generate patient-specific and disease-specific concepts, enabling the detection of pathognomonic cells and localized cellular abnormalities at patch-level. We quantified the effect of concepts on a patient-level AML subtype classification task and show that CytoSAE concepts reach performance comparable to the state-of-the-art, while offering explainability on the sub-cellular level. Source code and model weights are available athttps://github.com/dynamical-inference/cytosae.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Disease Classification ; Explainability ; Sparse Autoencoders
Sprache englisch
Veröffentlichungsjahr 2026
HGF-Berichtsjahr 2026
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Konferzenzdatum 23-27 September 2025
Konferenzort Daejeon
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15973 LNCS, Heft: , Seiten: 77-86 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute of Computational Biology (ICB)
Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
G-540007-001
DOI 10.1007/978-3-032-05185-1_8
Scopus ID 105018085373
Erfassungsdatum 2025-10-23
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