Weakly supervised deep learning for COVID-19 infection detection and classification from CT images
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.
Date Issued
2020-07-08
Date Acceptance
2020-06-25
Citation
IEEE Access, 2020, 8, pp.118869-18883
ISSN
2169-3536
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
118869
End Page
18883
Journal / Book Title
IEEE Access
Volume
8
Copyright Statement
© 2020 IEEE. This article is free to access and download, along with rights for full text and data mining, re-use and analysis. Under a Creative Commons License (CC-BY) https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
http://arxiv.org/abs/2004.06689v1
Subjects
eess.IV
eess.IV
cs.CV
cs.LG
Notes
21 pages, 7 figures
Publication Status
Published
Date Publish Online
2020-06-29