Zhu, Xiaofeng; Song, Bin; Shi, Feng; Chen, Yanbo; Hu, Rongyao; Gan, Jiangzhang; Zhang, Wenhai; Li, Man; Wang, Liye; Gao, Yaozong; Shan, Fei; Shen, Dinggang
Medical image analysis
2020Oct ; 67 ( 10 ) :101824.
PMID : 33091741
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Zhu, Xiaofeng - Center for Future Media and school of computer science and technology, University of Electronic Science and Technology of China, Chengdu 611731, China. Electronic address: seanzhuxf@gmail.com.
Song, Bin - Department of Radiology, Sichuan University West China Hospital, Chengdu 610041,
Shi, Feng - Department of Research and Development, Shanghai United Imaging Intelligence Co.,
Chen, Yanbo - Department of Research and Development, Shanghai United Imaging Intelligence Co.,
Hu, Rongyao - Center for Future Media and school of computer science and technology, University of
Gan, Jiangzhang - Center for Future Media and school of computer science and technology, University of
Zhang, Wenhai - Department of Research and Development, Shanghai United Imaging Intelligence Co.,
Li, Man - Department of Research and Development, Shanghai United Imaging Intelligence Co.,
Wang, Liye - Department of Research and Development, Shanghai United Imaging Intelligence Co.,
Gao, Yaozong - Department of Research and Development, Shanghai United Imaging Intelligence Co.,
Shan, Fei - Department of Radiology, Shanghai Public Health Clinical Center, Fudan University,
Shen, Dinggang - Department of Research and Development, Shanghai United Imaging Intelligence Co.,
ABSTRACT
With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e.,?the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g.,?85.91% of accuracy) and regression (e.g.,?0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time. CI - Copyright ??2020 Elsevier B.V. All rights reserved.
keyword
CT Scan data; Coronavirus disease; Feature selection; Imbalance classification; Sample selection
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