公共自我监督病理学基础模型的临床基准
Gabriele Campanella1,2, Shengjia Chen3,4, Manbir Singh3,4
1Windreich Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA. gabriele.campanella@mssm.edu.
Nature communications
|April 16, 2025
概括
通过自我监督学习训练的公开可用的病理学基础模型正在推进计算病理学. 这项研究将这些模型与各种临床数据集进行基准测试,为研究和部署提供了洞察力.
科学领域:
- 医学中的计算病理学和人工智能.
- 在医疗保健中开发和应用基础模型.
背景情况:
- 病理学基础模型的自我监督学习已经迅速发展.
- 最近公开发布的大规模,经过临床训练的基础模型.
- 越来越需要对这些模型进行标准化评估,以确定其临床效用.
研究的目的:
- 建立一个基准来比较公共病理学基金会模型的表现.
- 评估模型在多种器官和多种疾病中对不同,临床相关任务的性能.
- 提供关于培训和选择基础模型的最佳实践的见解.
主要方法:
- 编制了来自三个医疗中心的病理学数据集的综合集合.
- 数据集包括具有终点的临床幻灯片,如癌症诊断和生物标志物.
- 系统地评估公开可用的病理学基础模型的性能.
主要成果:
- 展示了不同公共病理学基础模型的性能变化.
- 确定了影响模型在临床任务上的表现的关键因素.
- 为选择合适的预训练模型提供数据驱动的见解.
结论:
- 公共基础模型的可用性加速了计算病理学研究.
- 标准化基准测试对于弥合研究和临床部署之间的差距至关重要.
- 发布了一个自动化的基准测试管道,以促进社区模型评估.
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