id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-176636-wzuhnfwp	Malhotra, Aakarsh	Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images	2020-08-03		.txt	text/plain	5069	369	56	authors: Malhotra, Aakarsh; Mittal, Surbhi; Majumdar, Puspita; Chhabra, Saheb; Thakral, Kartik; Vatsa, Mayank; Singh, Richa; Chaudhury, Santanu; Pudrod, Ashwin; Agrawal, Anjali Fig. 1 shows samples of chest x-ray images with different lung abnormalities including COVID-19. In this research, we propose a deep learning network termed as COVID-19 Multi-Task Network (CMTNet), which learns the abnormalities present in the chest x-ray images to differ-entiate between a COVID-19 affected lung and a Non-COVID affected lung. The proposed CMTNet simultaneously processes the input X-ray for semantic lung segmentation, disease localization, and healthy/unhealthy classification. 1) Develop COVID-19 Multi-Task Network (CMTNet) for classification and segmentation of the lung and disease 1 regions. 5) Creating and publicly releasing manual annotations for lung semantic segmentation for healthy, unhealthy, and COVID-19 affected X-ray images. The four tasks of CMTNet are (i) lung localization, (ii) disease localization, (iii) healthy/unhealthy classification and (iv) multi-label classification for COVID-19 prediction.	./cache/cord-176636-wzuhnfwp.txt	./txt/cord-176636-wzuhnfwp.txt