Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images.

Hu, Kai; Huang, Yingjie; Huang, Wei; Tan, Hui; Chen, Zhineng; Zhong, Zheng; Li, Xuanya; Zhang, Yuan; Gao, Xieping

Hu, Kai; Huang, Yingjie; Huang, Wei; Tan, Hui; Chen, Zhineng; Zhong, Zheng; Li, Xuanya; Zhang, Yuan; Gao, Xieping.

Afiliación

Hu K; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China.
Huang Y; Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province, Xiangnan University, Chenzhou 423000, China.
Huang W; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China.
Tan H; Department of Radiology, the First Hospital of Changsha, Changsha 410005, China.
Chen Z; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China.
Zhong Z; School of Computer Science, Fudan University, Shanghai 200438, China.
Li X; Department of Radiology, the First Hospital of Changsha, Changsha 410005, China.
Zhang Y; Baidu Inc, Beijing 100085, China.
Gao X; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China.

Neurocomputing (Amst) ; 458: 232-245, 2021 Oct 07.

Article en En | MEDLINE | ID: mdl-34121811

Palabras clave

COVID-19; Classification; Data imbalance; Deep supervised learning; Self-adaptive auxiliary loss

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Neurocomputing (Amst) Año: 2021 Tipo del documento: Article País de afiliación: China

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