Article

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types

2023-08-23

Abstract excerpt

As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Such automation provides an opportunity to learn fundamental biology about how tissue structure and function varies in a population. Recently, self-supervised learning has proven competitive to supervised machine learning...

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Literature Corpus work: 84f09554-ba21-5ecc-b365-23d8e4a5c85a
DOI: 10.1101/2023.08.22.554251

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