Multiple instance learning using pathology foundation models effectively predicts kidney disease diagnosis and clinical classification.

ABSTRACT: Recently developed pathology foundation models, pretrained on large-scale pathology datasets, have demonstrated excellent performance in various downstream tasks. This study evaluated the utility of pathology foundation models combined with multiple instance learning (MIL) for kidney pathology analysis. We used 242 hematoxylin and eosin-stained whole slide images (WSIs) from the Kidney Precision Medicine Project (KPMP) and Japan-Pathology Artificial Intelligence Diagnostics Project databases as the development cohort, comprising 47 healthy controls, 35 acute interstitial nephritis, and 160 diabetic kidney disease (DKD) slides. External validation was performed using 83 WSIs from the University of Tokyo Hospital. Pretrained pathology foundation models were utilized as patch encoders and com

SUBMITTER: Kurata Y 

PROVIDER: S-EPMC12511320 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature