While deep learning techniques have provided the state-of-the-art performance in various clinical tasks, explainability regarding their decision-making process can greatly enhance the credence of these methods for safer and quicker clinical adoption. With high flexibility, Gradient-weighted Class Activation Mapping (Grad-CAM) has been widely adopted to offer intuitive visual interpretation of various deep learning models' reasoning processes in computer-assisted diagnosis. However, despite the popularity of the technique, there is still a lack of systematic study on Grad-CAM's performance on different deep learning architectures. In this study, we investigate its robustness and effectiveness across different popular deep learning models, with a focus on the impact of the networks' depths and architecture types, by using a case study of automatic pneumothorax diagnosis in X-ray scans. Our results show that deeper neural networks do not necessarily contribute to a strong improvement of pneumothorax diagnosis accuracy, and the effectiveness of GradCAM also varies among different network architectures.

对不同深度学习模型的Gradient-weighted Class Activation Mapping (Grad-CAM)方法在自动胸部X光扫描中的气胸诊断中的鲁棒性和有效性进行了研究，结果显示深度神经网络并不一定能够显著提高气胸诊断的准确性，Grad-CAM的有效性也随着网络架构的不同而变化。

使用Grad-CAM进行视觉解释是否更可靠于深度神经网络？自动气胸诊断的案例研究