부산대학교 도서관

Despite significant progress in the past few years, machine learning systems are still often viewed as "black boxes," which lack the ability to explain their output decisions. In high-stakes situations such as healthcare, there is a need for explainable AI (XAI) tools that can help open up this black box. In contrast to approaches which largely tackle classification problems in the medical imaging domain, we address the less-studied problem of explainable image retrieval. We test our approach on a COVID-19 chest X-ray dataset and the ISIC 2017 skin lesion dataset, showing that saliency maps help reveal the image features used by models to determine image similarity. We evaluated three different saliency algorithms, which were either occlusion-based, attention-based, or relied on a form of activation mapping. We also develop quantitative evaluation metrics that allow us to go beyond simple qualitative comparisons of the different saliency algorithms. Our results have the potential to aid clinicians when viewing medical images and addresses an urgent need for interventional tools in response to COVID-19. The source code is publicly available at: https://gitlab.kitware.com/brianhhu/x-mir.