评估基于梯度的解释方法,用于使用热图进行神经网络心电图分析
Andrea Marheim Storås1,2, Steffen Mæland3, Jonas L Isaksen4
1Department of Holistic Systems, SimulaMet, 0170 Oslo, Norway.
Journal of the American Medical Informatics Association : JAMIA
|November 6, 2024
概括
对心电图 (ECG) 分析的深度神经网络解释方法的评估表明没有单一的最佳方法. 建议应用多种解释方法,以获得医疗AI的最佳结果.
科学领域:
- 人工智能在医学中的应用
- 生物医学信号处理
背景情况:
- 深度神经网络 (DNN) 越来越多地用于心电图 (ECG) 分析.
- 解释DNN预测对于临床采用和信任至关重要.
- 热图可视化常用于解释DNN输出.
研究的目的:
- 评估基于DNN的ECG分析的流行的解释方法.
- 将定性专家评估与基于干扰的客观评估进行比较.
- 为选择医学AI中的解释方法提供建议.
主要方法:
- 一个残留的DNN被训练用于ECG间隔和幅度预测.
- 评估了9种解释方法 (Saliency,Deconvolution,引导反向传播,梯度SHAP,SmoothGrad,输入×梯度,DeepLIFT,集成梯度,GradCAM).
- 医疗专家对方法进行了定性评估,并通过扰动对方法进行了定量评估.
主要成果:
- 没有一个单一的解释方法始终优于其他方法;有些方法比其他方法差.
- 在人类专家评估和客观基于扰乱的评估之间观察到显著的分歧.
- 方法性能因预测的特定ECG测量而有所不同.
结论:
- 最佳的解释方法取决于ECG测量的上下文.
- 数据科学家和医学专家之间的合作对于开发有用的解释方法至关重要.
- 建议采用多种解释方法,以确保医疗AI的强大和可靠的解释.
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