id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-270530-abiuiiff	Fan, D.-P.	Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans	2020-04-27		.txt	text/plain	6153	374	54	To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT scans. To address above issues, we propose a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) for CT Scans. Moreover, to alleviate the shortage of labeled data, we also provide a semi-supervised segmentation system, which only requires a few labeled COVID-19 infection images and then enables the model to leverage unlabeled data. Therefore, we extend Semi-Inf-Net to a multi-class lung infection labeling framework so that it can provide richer information for the further diagnosis and treatment of COVID-19. Specifically, we utilize the infection segmentation results provided by Semi-Inf-Net to guide the multi-class labeling of different types of lung infections. This framework can take full advantage of the infection segmentation results provided by Semi-Inf-Net and effectively improve the performance of multi-class infection labeling.	./cache/cord-270530-abiuiiff.txt	./txt/cord-270530-abiuiiff.txt