在肾脏病理学中通过人在环富集评估细胞AI基础模型
Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment
AI总结：
本文通过在包含2,542张肾脏全切片图像的多中心、多疾病、多物种数据集上评估Cellpose、StarDist和CellViT三种细胞基础模型，并结合人在环数据富集策略进行微调，建立了肾脏病理细胞分割基准并提升了模型性能。
摘要：
训练AI基础模型已成为应对现实世界医疗挑战（包括数字病理学）的一种有前景的大规模学习方法。尽管许多此类模型已利用广泛多样的训练数据集为疾病诊断和组织定量等任务而开发，但它们在部署于一些可以说是最简单的任务（例如单个器官（如肾脏）内的细胞核分割）上的就绪程度仍不确定。本文旨在通过在一个精心整理的多中心、多疾病、多物种外部测试数据集上全面评估近期细胞基础模型的性能，来回答这一关键问题：“我们做得有多好？”此外，我们通过开发和评估旨在提升模型性能同时尽量减少对像素级人工标注依赖的人在环数据富集策略，来解决一个更具挑战性的问题：“我们如何改进？”为回答第一个问题，我们整理了一个包含2,542张肾脏全切片图像（WSIs）的多中心、多疾病、多物种数据集。选取了三种最先进的（SOTA）细胞基础模型——Cellpose、StarDist和CellViT——进行评估。为解决第二个问题，我们探索了数据富集算法，通过在人在环框架中蒸馏不同基础模型的预测，旨在以最少的人工努力进一步提升基础模型性能。我们的实验结果表明，通过使用富集数据进行模型微调，所有三个基础模型都相较于其基线有所提升。有趣的是，基线模型中F1分数最高的模型在微调后并未产生最佳的分割结果。本研究为面向真实世界数据应用而定制的细胞视觉基础模型的开发和部署建立了一个基准。
英文摘要：
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 evaluation. To tackle the second question, we explored data enrichment algorithms by distilling predictions from the different foundation models with a human-in-the-loop framework, aiming to further enhance foundation model performance with minimal human efforts. Our experimental results showed that all three foundation models improved over their baselines with model fine-tuning with enriched data. Interestingly, the baseline model with the highest F1 score does not yield the best segmentation outcomes after fine-tuning. This study establishes a benchmark for the development and deployment of cell vision foundation models tailored for real-world data applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
