TY - JOUR
AU - Ye, Qinghao
AU - Gao, Yuan
AU - Ding, Weiping
AU - Niu, Zhangming
AU - Wang, Chengjia
AU - Jiang, Yinghui
AU - Wang, Minhao
AU - Fang, Evandro Fei
AU - Menpes-Smith, Wade
AU - Xia, Jun
AU - Yang, Guang
PY - 2022
DA - February
TI - Robust weakly supervised learning for COVID-19 recognition using multi-center CT images
JO - Applied Soft Computing
VL - 116
DO - https://doi.org/10.1016/j.asoc.2021.108291
AB - 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.
PB - Elsevier BV
UR - https://researchportal.hw.ac.uk/en/publications/eafeacbe-8e88-45b8-b6f9-7e2298c9c97f
KW - Coronavirus (COVID-19)
ER