SimulaMet, 0167, Oslo, Norway.
Oslo Metropolitan University, 0167, Oslo, Norway.
Sci Rep. 2021 May 26;11(1):10949. doi: 10.1038/s41598-021-90285-5.
Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.
基于深度学习的工具可以比医生更快、更一致、更准确地标注和解释医学数据。然而，由于医生最终要对临床决策负责，因此任何基于深度学习的预测都应该附有一个人类可以理解的解释。我们提出了一种称为心电图梯度类激活映射（ECGradCAM）的方法，用于生成注意力图并解释心电图分析中基于深度学习的决策背后的推理。注意力图可用于临床辅助诊断、发现新的医学知识以及识别医学测试的新特征和特性。在本文中，我们展示了 ECGradCAM 注意力图如何揭示新型深度学习模型如何测量 12 导联心电图中的幅度和间隔，以及如何使用注意力图开发新的心电图特征。
