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

ABSTRACT: 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. Here, we learn self-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, we can automatically segment tissues into their constituent tissue substructures and pathology proportions across thousands of whole slide images, outperforming other self-supervised methods (43% increase in silhouette score). Additionally, we can detect and quantify histological pathologies present, such as arterial calcification (AUROC = 0.93) and identify missing cal

SUBMITTER: Cisternino F 

PROVIDER: S-EPMC11246527 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature