Discovering Unknown Diseases with Explainable Automated Medical Imaging

Tang, Claire

Deep neural network (DNN) classifiers have attained remarkable performance in diagnosing known diseases when the models are trained on a large amount of data from known diseases. However, DNN classifiers trained on known diseases usually fail when they confront new diseases such as COVID-19. In this paper, we propose a new deep learning framework and pipeline for explainable medical imaging that can classify known diseases as well as detect new/unknown diseases when the models are only trained on known disease images. We first provide in-depth mathematical analysis to explain the overconfidence phenomena and present the calibrated confidence that can mitigate the overconfidence. Using calibrated confidence, we design a decision engine to determine if a medical image belongs to some known diseases or a new disease. At last, we introduce a new visual explanation to further reveal the suspected region inside each image. Using both Skin Lesion and Chest X-Ray datasets, we validate that our framework significantly improves the accuracy of new disease discovery, i.e., distinguish COVID-19 from pneumonia without seeing any COVID-19 data during training. We also qualitatively show that our visual explanations are highly consistent with doctors’ ground truth. While our work was not designed to target COVID-19, our experimental validation using the real world COVID-19 cases/data demonstrates the general applicability of our pipeline for different diseases based on medical imaging.