id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-157444-huvnyali	Nabulsi, Zaid	Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases	2020-10-22		.txt	text/plain	6802	326	44	In this work, we evaluated the DLS's performance on 6 independent test sets consisting of different patient populations, spanning three countries, and with two unseen diseases (TB and COVID-19). However, as other acute diseases may share a similar clinical presentation, many cases negative for COVID-19 exhibited abnormal CXR findings that likely triggered the DLS ( Figure 5, Supplementary Figure 5 ). Finally, to facilitate the continued development of AI models for chest radiography, we are releasing our abnormal versus normal labels from 3 radiologists (2430 labels on 810 images) for the publicly-available CXR-14 test set. Two datasets were used to evaluate the DLS's performance in distinguishing normal and abnormal findings in a general abnormality detection setting. To compare the DLS with radiologists in classifying CXRs as normal versus abnormal, additional radiologists reviewed all test images without referencing additional clinical or patient data.	./cache/cord-157444-huvnyali.txt	./txt/cord-157444-huvnyali.txt