基准基准模型作为弱监督计算病理学的特征提取器
Peter Neidlinger1, Omar S M El Nahhas1,2, Hannah Sophie Muti1,3,4
1Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Nature biomedical engineering
|October 1, 2025
概括
在不同癌症队伍中对19个组织病理学基础模型进行基准测试,发现CONCH等视觉语言模型优于仅视觉模型. 数据多样性是关键,模型融合提高了性能,这表明了未来对病理学AI的改进.
科学领域:
- 数字病理学数字病理学
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 基础模型越来越多地用于从组织病理学图像中提取临床信息.
- 在外部数据集和任务上对这些模型的独立评估是有限的,阻碍了进展.
- 基准测试对于理解模型性能和确定需要改进的领域至关重要.
研究的目的:
- 为了全面对19个组织病理学基础模型进行基准测试.
- 评估不同患者队列和癌症类型 (肺,结肠直肠,胃,乳腺) 的模型性能.
- 评估模型在弱监督任务中的能力,包括生物标志物预测,形态分析和预后结果确定.
主要方法:
- 在13个患者队列中评估了19个基础模型,包括6818名患者和9528个幻灯片.
- 利用弱监督的学习任务,专注于生物标志物,形态和预后.
- 视觉语言模型与仅视觉模型的性能比较.
主要成果:
- 视觉语言模型CONCH显示了最高的整体性能,紧随其后的是Virchow2.
- 在低数据或低患病率的场景中,性能优势不那么明显.
- 在不同队伍中训练的基础模型学习了互补的特征,通过融合实现了性能提升.
- 一组CONCH和Virchow2在55%的任务中表现优于单个模型.
- 发现数据多样性对于基础模型开发来说比数据量更为关键.
结论:
- 与仅视觉模型相比,视觉语言基础模型在组织病理学任务中表现优越.
- 结合互补模型的合并方法可以显著提高预测性能.
- 优先考虑数据多样性而不是大量数据,对于开发数字病理学的强大基础模型至关重要.
- 这项研究为计算病理学的未来基础模型开发和评估提供了一个基准.
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