An end-to-end multi-task system of automatic lesion detection and anatomical localization in whole-body bone scintigraphy by deep learning.

Huang, Kaibin; Huang, Shengyun; Chen, Guojing; Li, Xue; Li, Shawn; Liang, Ying; Gao, Yi

Huang, Kaibin; Huang, Shengyun; Chen, Guojing; Li, Xue; Li, Shawn; Liang, Ying; Gao, Yi.

Afiliação

Huang K; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518037, China.
Huang S; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China.
Chen G; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518037, China.
Li X; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518037, China.
Li S; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518037, China.
Liang Y; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China.
Gao Y; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

Bioinformatics ; 39(1)2023 01 01.

Article em En | MEDLINE | ID: mdl-36416135

Assuntos

Aprendizado Profundo; Estudos Retrospectivos; Processamento de Imagem Assistida por Computador/métodos; Algoritmos; Cintilografia; Aprendizado de Máquina Supervisionado

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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