In this section
@article{Hu:2020:10.1109/ACCESS.2020.3005510,
author = {Hu, S and Gao, Y and Niu, Z and Jiang, Y and Li, L and Xiao, X and Wang, M and Fang, EF and Menpes-Smith, W and Xia, J and Ye, H and Yang, G},
doi = {10.1109/ACCESS.2020.3005510},
journal = {IEEE Access},
pages = {118869--18883},
title = {Weakly supervised deep learning for COVID-19 infection detection and classification from CT images},
url = {http://dx.doi.org/10.1109/ACCESS.2020.3005510},
volume = {8},
year = {2020}
}
TY - JOUR
AB - 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.
AU - Hu,S
AU - Gao,Y
AU - Niu,Z
AU - Jiang,Y
AU - Li,L
AU - Xiao,X
AU - Wang,M
AU - Fang,EF
AU - Menpes-Smith,W
AU - Xia,J
AU - Ye,H
AU - Yang,G
DO - 10.1109/ACCESS.2020.3005510
EP - 18883
PY - 2020///
SN - 2169-3536
SP - 118869
TI - Weakly supervised deep learning for COVID-19 infection detection and classification from CT images
T2 - IEEE Access
UR - http://dx.doi.org/10.1109/ACCESS.2020.3005510
UR - http://arxiv.org/abs/2004.06689v1
UR - http://hdl.handle.net/10044/1/80217
VL - 8
ER -
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