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

ABSTRACT: 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 methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features whic

SUBMITTER: Claudio Quiros A 

PROVIDER: S-EPMC11525555 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature