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Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams.
Liu, Simin; Feng, Ming; Qiao, Tingting; Cai, Haidong; Xu, Kele; Yu, Xiaqing; Jiang, Wen; Lv, Zhongwei; Wang, Yin; Li, Dan.
Afiliação
  • Liu S; Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
  • Feng M; School of Electronic and Information Engineering, Tongji University, Shanghai, People's Republic of China.
  • Qiao T; Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
  • Cai H; Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
  • Xu K; National Key Laboratory of Parallel and Distributed Processing, National University of Defense Technology, Changsha, People's Republic of China.
  • Yu X; Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
  • Jiang W; Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
  • Lv Z; Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
  • Wang Y; School of Electronic and Information Engineering, Tongji University, Shanghai, People's Republic of China.
  • Li D; Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
Cancer Manag Res ; 14: 51-65, 2022.
Article em En | MEDLINE | ID: mdl-35018121
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article