Open AccessAugmenting Interpretation of Chest Radiographs With Deep Learning Probability Maps
Author(s) -
Brian Hurt,
Andrew Yen,
Seth Kligerman,
Albert Hsiao
Publication year - 2020
Publication title -
journal of thoracic imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 57
eISSN - 1536-0237
pISSN - 0883-5993
DOI - 10.1097/rti.0000000000000505
Subject(s) - medicine , radiography , artificial intelligence , receiver operating characteristic , pneumonia , convolutional neural network , radiology , deep learning , segmentation , binary classification , computer science , support vector machine
Pneumonia is a common clinical diagnosis for which chest radiographs are often an important part of the diagnostic workup. Deep learning has the potential to expedite and improve the clinical interpretation of chest radiographs. While earlier approaches have emphasized the feasibility of "binary classification" to accomplish this task, alternative strategies may be possible. We explore the feasibility of a "semantic segmentation" deep learning approach to highlight the potential foci of pneumonia on frontal chest radiographs.