id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-028803-l92jcw9h	Tang, Claire	Discovering Unknown Diseases with Explainable Automated Medical Imaging	2020-06-09		.txt	text/plain	3456	217	54	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 develop an automatic visual explanation into deep learning models to reveal suspected evidence in medical images for potential unknown diseases. -Based on our proposed new pipeline, we conduct comprehensive experimental evaluations showing that our system achieves significant performance improvement on both quantitatively (unknown disease detection) and qualitatively (visual explanation) on Skin Lesion and Chest X-Ray datasets. Then, we use the following "softmax function" [7] to normalize the logits to be a probability distribution: We illustrate our overconfidence explanation in Fig. 2 using an example: Assuming there are two indomain classes in our classifier. GC improved baseline performance by over 6 times on Chest X-Ray to detect new out-of-domain COVID-19 disease using the model trained on known pneumonia and normal images.	./cache/cord-028803-l92jcw9h.txt	./txt/cord-028803-l92jcw9h.txt