DP-ProtoNet: An interpretable dual path prototype network for medical image diagnosis

The significant success of deep learning has sparked interest in its application in medical diagnosis. Some deep learning models have achieved expert-level accuracy on some medical datasets, but these models are rarely used in clinical practice due to the lack of interpretability. Therefore, the research topic of explainable artificial intelligence (XAI) has emerged to make the reasoning process of the model transparent and interpretable. In this case, we applied interpretable artificial intelligence to dermatoscopy image diagnosis for the first time. Specifically, we use an interpretable prototype network for dermoscopy image diagnosis. To solve the problem of weak generalization performance of a single network, we propose to construct a new prototype network using the dual-path network. Besides, we propose a new gate similarity calculation method to reduce the activation of low-similarity regions, thereby reducing the generation of inaccurate prototypes and improving the diagnostic ability of the model. We conducted experiments on the dermoscopy datasets HAM10000 to test the model and compare it with other baseline models. Experimental results show that DP-ProtoNet has made improvements in accuracy while preserving the interpretability of the model.