Evaluating cell AI foundation models in kidney pathology with human-in-the-loop enrichment.

ABSTRACT:

Background

Large-scale artificial intelligence foundation models have emerged as promising tools for addressing healthcare challenges, including digital pathology. While many have been developed for complex tasks such as disease diagnosis and tissue quantification using extensive and diverse datasets, their readiness for seemingly simpler tasks, such as nuclei segmentation within a single organ (for example, the kidney), remains unclear. This study answers two questions: How good are current cell foundation models? and How can we improve them?

Methods

We curated a multi-center, multi-disease, and multi-species dataset sampled from 2542 kidney whole slide images. Three state-of-the-art cell foundation models-Cellpose, StarDist, and CellViT-were evaluated. To enhance performan

SUBMITTER: Guo J 

PROVIDER: S-EPMC12644679 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature