Robust weakly supervised learning for COVID-19 recognition using multi-center CT images
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.
Date Issued
2022-02-01
Date Acceptance
2021-12-07
Citation
Applied Soft Computing, 2022, 116
ISSN
1568-4946
Publisher
Elsevier
Journal / Book Title
Applied Soft Computing
Volume
116
Copyright Statement
©2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
License URL
Sponsor
British Heart Foundation
European Research Council Horizon 2020
Commission of the European Communities
Innovative Medicines Initiative
Boehringer Ingelheim Ltd
Medical Research Council (MRC)
Grant Number
PG/16/78/32402
H2020-SC1-FA-DTS-2019-1 952172
101005122
101005122
PO:4700244755 Study:1199-0457
MR/V023799/1
Subjects
eess.IV
eess.IV
cs.CV
cs.LG
0102 Applied Mathematics
0801 Artificial Intelligence and Image Processing
0806 Information Systems
Artificial Intelligence & Image Processing
Publication Status
Published
Article Number
ARTN 108291
Date Publish Online
2021-12-13