Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images.

Liu, Xiaoming; Yuan, Quan; Gao, Yaozong; He, Kelei; Wang, Shuo; Tang, Xiao; Tang, Jinshan; Shen, Dinggang

Liu, Xiaoming; Yuan, Quan; Gao, Yaozong; He, Kelei; Wang, Shuo; Tang, Xiao; Tang, Jinshan; Shen, Dinggang.

Afiliação

Liu X; School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China.
Yuan Q; Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China.
Gao Y; School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China.
He K; Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China.
Wang S; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Tang X; Medical School, Nanjing University, Nanjing, China.
Tang J; National Institute of Healthcare Data Science at Nanjing University, Nanjing, China.
Shen D; School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China.

Pattern Recognit ; 122: 108341, 2022 Feb.

Article em En | MEDLINE | ID: mdl-34565913

Palavras-chave

COVID-19; infection segmentation; transformation consistency; uncertainty; weakly supervised learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: Pattern Recognit Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido

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