超越准确性:量化多个实例学习对整个幻灯片图像分类的可靠性
Hassan Keshvarikhojasteh1, Marc Aubreville2, Christof A Bertram3
1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
PloS one
|December 5, 2025
概括
机器学习模型在病理学中的可靠性至关重要. 平均聚合实例 (MEAN-POOL-INS) 模型显示了全幻灯片图像分类的卓越可靠性,提供了可靠的基线.
科学领域:
- 计算病理学计算病理学
- 机器学习 机器学习
- 医学成像分析分析 医学成像分析
背景情况:
- 机器学习 (ML) 模型被广泛使用,但其可靠性令人担忧.
- 整个幻灯片图像 (WSI) 分类的多个实例学习 (MIL) 模型缺乏可靠性评估.
- 这种差距阻碍了它们在临床决策中的使用.
研究的目的:
- 引入量化指标来评估MIL模型的可靠性.
- 在病理学数据集上评估常见MIL架构的可靠性.
- 为了确定可靠的MIL模型用于WSI分类.
主要方法:
- 开发了用于可靠性评估的三种新的定量指标.
- 应用于多个MIL架构 (例如,MEAN-POOL-INS) 的指标.
- 为了评估,利用了三个区域性注释病理学数据集.
主要成果:
- 平均聚合实例 (MEAN-POOL-INS) 模型表现出卓越的可靠性.
- 尽管设计简单且效率高,但MEAN-POOL-INS表现出高可靠性.
- 在不同的MIL架构和数据集中,可靠性有所不同.
结论:
- 可靠性评估对于计算病理学的MIL模型至关重要.
- MEAN-POOL-INS作为WSI分类的可靠和高效的基准.
- 这些发现支持可靠的MIL模型的临床适用性.
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