培训经过认证的侦探来追踪COVID-19胸部X射线数据集中的内在捷径
Ran Zhang1, Dalton Griner1, John W Garrett1,2
1Department of Medical Physics, School of Medicine and Public Health, The University of Wisconsin in Madison, Madison, WI, 53705, USA.
Scientific reports
|August 4, 2023
概括
由于虚假的相关性,深度学习模型在新数据上失败. 这项研究表明,胸部X射线中的图像对比度和清晰度可能是快捷方式,阻碍了COVID-19分类模型的概括性.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 深度学习模型通常在外部数据集上表现不佳,这种现象与非相关特征和标签之间的虚假相关性有关.
- 在医学成像中,像图像对比度和清晰度这样的特征可能无意中成为模型的快捷方式,损害其概括能力.
研究的目的:
- 研究图像采集特征,特别是胸部X射线中的对比度和清晰度,如何作为内在的快捷方式,损害深度学习模型的概括性.
- 为了证明这一现象,使用胸部X射线图的COVID-19分类的例子来说明这一现象.
主要方法:
- 利用胸部X射线放射图作为一个案例研究,用于COVID-19的分类.
- 分析了图像对比度和清晰度作为影响模型性能的潜在混因素.
- 提出了一种培训经过认证的捷径侦探模型的方法,具有特定的资格标准.
主要成果:
- 证明胸部X射线图像的图像对比度和清晰度可以作为内在的快捷方式,导致训练有素的深度学习模型的概括性降低.
- 确定这些图像特征是外部测试数据集模型表现不佳的关键因素.
结论:
- 该研究强调了解决内在快捷方式的关键需求,例如图像采集参数,以提高医疗诊断中的深度学习模型的稳定性和可靠性.
- 拟议的认证捷径侦探模型提供了一个潜在的解决方案,用于在精心策划的数据集中识别和减轻这些虚假的相关性.
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