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Koch, V. ; Bauer, S.* ; Mahajan, S. ; Lupperger, V.* ; Joner, M.* ; Schunkert, H.* ; Schnabel, J.A. ; von Scheidt, M.* ; Marr, C.

UNICORN: A deep learning model for integrating multi-stain data in histopathology.

NPJ Digit. Med., DOI: 10.1038/s41746-026-02829-6 (2026)
DOI PMC
Open Access Gold möglich sobald Verlagsversion bei der ZB eingereicht worden ist.
The integration of multi-stain histopathology images through deep learning poses a significant challenge. Current approaches struggle with data heterogeneity and missing data, as concatenating multi-stain features may not effectively model stain-specific and cross-stain interactions. We introduce UNICORN (UNiversal stain Integration network for CORonary classificatioN), a two-stage, end-to-end trainable model comprising transformer self-attention blocks to process multi-stain histopathology for atherosclerosis severity prediction. The initial stage employs domain-specific expert models to extract features from each staining. An aggregation expert model then integrates features by learning their interactions. On a multi-class, multi-stain whole slide images (WSIs) dataset of atherosclerotic lesions from Munich Cardiovascular Studies Biobank (MISSION), UNICORN achieved a classification accuracy of 0.68, significantly outperforming state-of-the-art models. UNICORN identifies relevant tissue phenotypes across stainings and implicitly models disease progression. Its explainability and effectiveness in predicting atherosclerosis progression highlight the potential for broader applications in medical research and decision support.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 2398-6352
e-ISSN 2398-6352
Zeitschrift NPJ digital medicine
Verlag Springer
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)