Hierarchical Salient Patch Identification for Interpretable Fundus Disease Localization

With the widespread application of deep learning technology in medical image analysis, how to effectively explain model decisions and improve diagnosis accuracy has become an urgent problem that needs to be solved. Attribution methods have become a key tool to help doctors better understand the diagnostic basis of models, and they are used to explain and localize diseases in medical images. However, previous methods suffer from inaccurate and incomplete localization problems for fundus diseases with complex and diverse structures. In order to solve the above problems, we propose a weakly supervised interpretable fundus disease localization method hierarchical salient patch identification (HSPI), which can achieve interpretable disease localization using only image-level labels and neural network classifiers. First, we proposed salient patch identification (SPI), which divides the image into several patches and optimizes consistency loss to identify which patch in the input image is most important for decision-making to locate the disease. Secondly, we propose a hierarchical identification strategy to force SPI to analyze the importance of different areas to neural network classifiers decision-making to comprehensively locate disease areas. Then, we introduced conditional peak focusing to ensure that the mask vector can accurately locate the decision area. Finally, we also propose patch selection based on multi-size intersection to filter out incorrectly or additionally identified non-disease regions. We conduct disease localization experiments on medical image datasets and achieve the best performance on multiple evaluation metrics compared with previous interpretable attribution methods. We performed additional ablation studies to verify the effectiveness of each method.