id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-189629-7qaqu02f	Tan, Tao	Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19	2020-11-10		.txt	text/plain	4741	215	49	title: Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 Artificial intelligence (AI) assisted X-ray based applications for triaging and monitoring require experienced radiologists to identify COVID patients in a timely manner and to further delineate the disease region boundary are seen as a promising solution. Although the performance of some systems approaches the level of radiologists on X-rays in terms of classification, but to our best knowledge, no studies have verified the detection and segmentation of the disease regions against human annotation on X-rays. For location, the network generates a segmentation mask to identify disease pixels or regions on X-ray related to COVID-19 or regular pneumonia. We evaluated our approach in three seperate aspects: first, AI predictions were compared with the image-level classification labels; second, the segmentation of disease regions for the COVID class was evaluated against direct X-ray pixel-wise annotations.	./cache/cord-189629-7qaqu02f.txt	./txt/cord-189629-7qaqu02f.txt