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Ding, T.* ; Wagner, S. ; Song, A.H.* ; Chen, R.J.* ; Lu, M.Y.* ; Zhang, A.* ; Vaidya, A.J.* ; Jaume, G.* ; Shaban, M.* ; Kim, A.* ; Williamson, D.F.K.* ; Robertson, H.* ; Chen, B.* ; Almagro-Pérez, C.* ; Doucet, P.* ; Sahai, S.* ; Chen, C.* ; Chen, C.S.* ; Komura, D.* ; Kawabe, A.* ; Ochi, M.* ; Sato, S.* ; Yokose, T.* ; Miyagi, Y.* ; Ishikawa, S.* ; Gerber, G.* ; Peng, T. ; Le, L.P.* ; Mahmood, F.*

A multimodal whole-slide foundation model for pathology.

Nat. Med., DOI: 10.1038/s41591-025-03982-3 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning. However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose Transformer-based pathology Image and Text Alignment Network (TITAN), a multimodal whole-slide foundation model pretrained using 335,645 whole-slide images via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any fine-tuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that it outperforms both ROI and slide foundation models across machine learning settings, including linear probing, few-shot and zero-shot classification, rare cancer retrieval, cross-modal retrieval and pathology report generation.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 1078-8956
e-ISSN 1546-170X
Journal Nature medicine
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-530006-001
DOI 10.1038/s41591-025-03982-3
PubMed ID 41193692
Erfassungsdatum 2025-11-07
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