Large-scale extraction of interpretable features provides new insights into kidney histopathology - A proof-of-concept study.

ABSTRACT: Whole slide images contain a magnitude of quantitative information that may not be fully explored in qualitative visual assessments. We propose: (1) a novel pipeline for extracting a comprehensive set of visual features, which are detectable by a pathologist, as well as sub-visual features, which are not discernible by human experts and (2) perform detailed analyses on renal images from mice with experimental unilateral ureteral obstruction. An important criterion for these features is that they are easy to interpret, as opposed to features obtained from neural networks. We extract and compare features from pathological and healthy control kidneys to learn how the compartments (glomerulus, Bowman's capsule, tubule, interstitium, artery, and arterial lumen) are affected by the pathology. We

SUBMITTER: Gupta L 

PROVIDER: S-EPMC9576990 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature