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Title: Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainty.  more » « less
Award ID(s):
1736196
NSF-PAR ID:
10402194
Author(s) / Creator(s):
; ; ; ;
Editor(s):
Hemanth, Jude
Date Published:
Journal Name:
PLOS ONE
Volume:
17
Issue:
11
ISSN:
1932-6203
Page Range / eLocation ID:
e0276250
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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