在数字病理学中用于分布外检测的扩散模型
Jasper Linmans1, Gabriel Raya2, Jeroen van der Laak3
1Department of Pathology, RadboudUMC Graduate School, Radboud University Medical Center, Nijmegen, The Netherlands.
Medical image analysis
|January 16, 2024
概括
无监督的新方法AnoDDPMs有效地检测到数字病理学图像中的分布外异常. 这种方法有望改善医疗成像中的机器学习部署,通过识别未见的数据而不需要注释.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 检测异常或分布外 (OOD) 数据对于在医学成像中部署机器学习至关重要.
- 无监督学习为OOD检测提供了一个有希望的,无注释的方法.
- 否认扩散概率模型 (DDPMs) 已经推进了无监督的OOD检测.
研究的目的:
- 在使用AnoDDPMs的数字病理学中比较无监督的OOD检测方法.
- 在Camelyon16挑战中评估AnoDDPMs在全幻灯片图像分析方面的性能.
- 评估AnoDDPMs在医疗图像中识别异常数据的能力.
主要方法:
- 基于DDPM的应用AnoDDPM,使用快速采样技术进行大规模全幻灯片图像分析.
- 在Camelyon16挑战数据集上进行了补丁级OOD检测任务.
- 使用接收器操作特征 (ROC) 分析和曲线下的面积 (AUC) 度量来评估模型性能.
主要成果:
- 在两个补丁级OOD检测任务中,AnoDDPMs实现了高AUC得分,高达94.13%和86.93%.
- 在检测OOD数据方面,AnoDDPMs的表现优于其他无监督方法.
- 观察到AnoDDPMs修改异常数据,使其看起来更为良性.
结论:
- 在数字病理学中,AnoDDPM显示了无监督OOD检测的巨大潜力.
- 该方法在不同的信息瓶和信号噪音比率方面表现出灵活性.
- 虽然与完全监督的方法不匹配,但AnoDDPMs代表了医学成像异常检测的有希望的进步.
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