A deep-learning-based framework for severity assessment of COVID-19 with CT images.

Li, Zhidan; Zhao, Shixuan; Chen, Yang; Luo, Fuya; Kang, Zhiqing; Cai, Shengping; Zhao, Wei; Liu, Jun; Zhao, Di; Li, Yongjie

Li, Zhidan; Zhao, Shixuan; Chen, Yang; Luo, Fuya; Kang, Zhiqing; Cai, Shengping; Zhao, Wei; Liu, Jun; Zhao, Di; Li, Yongjie.

Afiliação

Li Z; MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Zhao S; MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Chen Y; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Luo F; MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Kang Z; MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Cai S; Department of Radiology, Wuhan Red Cross Hospital, Wuhan, China.
Zhao W; Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China.
Liu J; Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China.
Zhao D; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Li Y; MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.

Expert Syst Appl ; 185: 115616, 2021 Dec 15.

Article em En | MEDLINE | ID: mdl-34334965

Palavras-chave

COVID-19; Clinical metadata; Deep learning; Dual-Siamese channels; Multi-view lesion; Severity assessment

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Expert Syst Appl Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China País de publicação: Estados Unidos

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