Self-supervised learning with large-scale web image mining for characterizing glomerular lesions

Abstract

Glomeruli are clusters of capillaries that are responsible for filtering the blood to form urine, thus excreting waste and maintaining fluid and acid-base balance. The detection and characterization of glomeruli are key elements in diagnostic and experimental nephropathology. Although the field of machine vision has already advanced the detection, classification, and prognostication of diseases in the specialties of radiology and oncology, renal pathology is just entering the digital imaging era. However, developing quantitative machine learning approaches (e.g., self-supervised deep learning) that characterize glomerular lesions (e.g., global glomerulosclerosis (GGS)) from whole slide images (WSIs) typically requires large-scale heterogeneous images, which is resource extensive for individual labs. In this study, we assess the feasibility of leveraging fine-grained GGS characterization via large-scale web image mining (e.g., from journals, search engines, websites) and self-supervised deep learning. Three types of GGS were assessed-solidified (S-GGS, associated with hypertension-related injury), disappearing (D-GGS, a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent (O-GGS, nonspecific GGS increasing with aging). We employed the SimSiam network as the baseline method of self-supervised contrastive learning. By deploying our previously developed compound figure separation approach, we provided 30,000 unannotated glomerular images via web image mining to train the SimSiam network. From the results, the GGS fine-grained classification model achieved superior performance compared with baseline methods. The segmentation networks evaluated across six different resolutions