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"Yeaton, C"
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Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides
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 which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.
Supervised deep learning models hold promise for the interpretation of histology images, but are limited by cost and quality of training datasets. Here, the authors develop a self-supervised deep learning method that can automatically discover features in cancer histology images that are associated with diagnosis, survival, and molecular phenotypes.
Journal Article
