Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer

Abstract Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features fr...

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Main Authors: Bojing Liu, Meaghan Polack, Nicolas Coudray, Adalberto Claudio Quiros, Theodore Sakellaropoulos, Hortense Le, Afreen Karimkhan, Augustinus S. L. P. Crobach, J. Han J. M. van Krieken, Ke Yuan, Rob A. E. M. Tollenaar, Wilma E. Mesker, Aristotelis Tsirigos
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-57541-y
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https://doi.org/10.1038/s41467-025-57541-y

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