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Article

Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights.

Scientific reports - 8 Oct 2025

Pandit Krutika, Coudray Nicolas, Quiros Adalberto Claudio, Surapaneni Aditya, Upadhyay Dhairya, Vanguri Rami Sesha, Hirohama Daigoro, Mohandes Samer, Schlosser Pascal, Thiessen-Philbrook Heather, Wen Yumeng, Parikh Chirag R, Rhee Eugene P, Waikar Sushrut S, Schmidt Insa, Rosenberg Avi Z, Palmer Matthew B, Susztak Katalin, Grams Morgan E, Tsirigos Aristotelis

Abstract excerpt

Deep learning methods for image segmentation and classification in histopathology generally utilize supervised learning, relying on manually created labels for model development. Here, we applied a self-supervised framework to characterize kidney histology without the use of pathologist annotations, training on whole slide images to identify histomorphological phenotype clusters (HPCs) and create slide-level...

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Identifiers

PMID: 41062686
DOI: 10.1038/s41598-025-19193-2
PMCID: PMC12508135

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