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SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables.
Hao, Degan; Li, Qiong; Feng, Qiu-Xia; Qi, Liang; Liu, Xi-Sheng; Arefan, Dooman; Zhang, Yu-Dong; Wu, Shandong.
Afiliação
  • Hao D; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA.
  • Li Q; Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, PR China.
  • Feng QX; Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, PR China.
  • Qi L; Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, PR China.
  • Liu XS; Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, PR China.
  • Arefan D; Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
  • Zhang YD; Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, PR China. Electronic address: zhangyd3895@njmu.edu.cn.
  • Wu S; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA; Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA; Department of Biomedical Informatics, University of Pit
Artif Intell Med ; 134: 102424, 2022 12.
Article em En | MEDLINE | ID: mdl-36462894
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Neoplasias Gástricas / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Artif Intell Med Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Holanda
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Neoplasias Gástricas / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Artif Intell Med Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Holanda