  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 uncertainties.
