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How Good Are We? Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment by Guo, Junlin; Lu, Siqi; Cui, Can; Deng, Ruining; Yao, Tianyuan; Tao, Zhewen; Lin, Yizhe; Lionts, Marilyn; Liu, Quan; Xiong, Juming; Wang, Yu; Zhao, Shilin; Chang, Catie; Wilkes, Mitchell; Yin, Mengmeng; Yang, Haichun; Huo, Yuankai is a scholarly article available to read on EtoBox.

What is How Good Are We? Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment about?

Training AI foundation models has emerged as a promising large-scale learning approach for addressing real-world healthcare challenges, including digital pathology. While many of these models have been developed for tasks like disease diagnosis and tissue quantification using extensive and diverse training datasets, their readiness for deployment on some arguably simplest tasks, such as nuclei segmentation within a single organ (e.g., the kidney), remains uncertain. This paper seeks to answer this key question, "How good are we?", by thoroughly evaluating the performance of recent cell foundation models on a curated multi-center, multi-disease, and multi-species external testing dataset. Additionally, we tackle a more challenging question, "How can we improve?", by developing and assessing human-in-the-loop data enrichment strategies aimed at enhancing model performance while minimizing the reliance on pixel-level human annotation. To address the first question, we curated a multicenter, multidisease, and multispecies dataset consisting of 2,542 kidney whole slide images (WSIs). Three state-of-the-art (SOTA) cell foundation models-Cellpose, StarDist, and CellViT-were selected for e

Author

Guo, Junlin; Lu, Siqi; Cui, Can; Deng, Ruining; Yao, Tianyuan; Tao, Zhewen; Lin, Yizhe; Lionts, Marilyn; Liu, Quan; Xiong, Juming; Wang, Yu; Zhao, Shilin; Chang, Catie; Wilkes, Mitchell; Yin, Mengmeng; Yang, Haichun; Huo, Yuankai

Published

2024

Language

EN