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Article

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.

Nature communications - 11 Jun 2024

Claudio Quiros Adalberto, Coudray Nicolas, Yeaton Anna, Yang Xinyu, Liu Bojing, Le Hortense, Chiriboga Luis, Karimkhan Afreen, Narula Navneet, Moore David A, Park Christopher Y, Pass Harvey, Moreira Andre L, Le Quesne John, Tsirigos Aristotelis, Yuan Ke

Abstract excerpt

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised...

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Identifiers

PMID: 38862472
DOI: 10.1038/s41467-024-48666-7
PMCID: PMC11525555

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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