Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

ABSTRACT:

Background

Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required for their creation. This limitation hinders the widespread application of deep learning for the analysis of kidney biopsy images.

Methods

We applied self-distillation with no labels (DINO), a self-supervised learning method, to a dataset of 10,423 glomerular images obtained from 384 periodic acid–Schiff-stained kidney biopsy slides. Glomerular features extracted from the DINO-pretrained backbone were visualized using principal component analysis. We then performed classification tasks by adding either k-nearest neighbor classifier

SUBMITTER: Abe M 

PROVIDER: S-EPMC11888952 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature