MMD-Net: a weakly supervised solution for quantification of nonalcoholic fatty liver biopsies.

ABSTRACT:

Background

Nonalcoholic fatty liver disease (NAFLD) affects 25% of the global population and is a leading cause of cirrhosis. Although liver biopsy remains the diagnostic gold standard, its clinical utility is limited by the labor-intensive Kleiner scoring system. Existing deep learning (DL) solutions face two critical barriers: pathologist-dependent annotations requiring equivalent time to manual assessment, and prohibitive costs for large-scale labeled datasets. The primary objective of this research was to establish a weakly supervised framework using multi-instance learning (MIL) for NAFLD assessment, aiming to reduce the annotation workload for pathologists while developing a clinically applicable diagnostic approach.

Methods

This study utilized a hepatocellular patholo

SUBMITTER: Yu M 

PROVIDER: S-EPMC12780620 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature