Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model.

Tang, Suigu; Yu, Xiaoyuan; Cheang, Chak-Fong; Hu, Zeming; Fang, Tong; Choi, I-Cheong; Yu, Hon-Ho

Tang, Suigu; Yu, Xiaoyuan; Cheang, Chak-Fong; Hu, Zeming; Fang, Tong; Choi, I-Cheong; Yu, Hon-Ho.

Afiliação

Tang S; Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China.
Yu X; Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China.
Cheang CF; Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China.
Hu Z; Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China.
Fang T; Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China.
Choi IC; Kiang Wu Hospital, Macau 999078, China.
Yu HH; Kiang Wu Hospital, Macau 999078, China.

Sensors (Basel) ; 22(4)2022 Feb 15.

Article em En | MEDLINE | ID: mdl-35214396

Assuntos

Aprendizado Profundo; Endoscopia; Humanos; Processamento de Imagem Assistida por Computador

Palavras-chave

classification; deep learning; esophageal lesions; gastrointestinal endoscopy; multi-task; segmentation

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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