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 vector representations. HPCs developed in the training set were visually consistent when transferred to five diverse internal and external validation sets (1,421 WSIs in total). Specific HPCs were reproducibly associated with slide-level pathologist quantifications, such as interstitial fibrosis (AUC = 0.83). Additionally, hierarchical clustering of tissue patterns revealed patient groups related to kidney function and genotype, and specific HPCs predicted longitudinal kidney function decline. Overall, we demonstrated the translational application of a self-supervised framework to summarize distinct kidney tissue patterns with phenotypic and prognostic relevance.

Aristotelis Tsirigos is a cofounder of Imagenomix.

https://github.com/AdalbertoCq/Histomorphological-Phenotype-Learning

This was an NIH-funded study.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee/IRB of the University of Pennsylvania gave ethical approval for this work. Ethics committee/IRB of Johns Hopkins University gave ethical approval for this work.

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Access to training data (whole slide images) used in the present study may be available upon request. The HPL pipeline can be replicated using the following GitHub repository - https://github.com/AdalbertoCq/Histomorphological-Phenotype-Learning. Models from the current study trained on kidney tissue tiles of 5X, 10X, and 20X magnification are available here - https://github.com/AdalbertoCq/Histomorphological-Phenotype-Learning/blob/master/README.md#other-pretrained-models.