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[Analysis of pancreatic segmentation algorithm based on deep learning to improve pancreatic critical region segmentation ability on dual-phase CT].
Wang, X H; Xue, H D; Qu, T P; Li, X L; Cheng, S H; Li, J; Zhu, L; Wu, Q L; Jin, Z Y.
Afiliação
  • Wang XH; Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medicine, Beijing, 100730, China.
  • Xue HD; Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medicine, Beijing, 100730, China.
  • Qu TP; Deepwise AI Lab, Deepwise Inc., Beijing, 100080, China.
  • Li XL; Deepwise AI Lab, Deepwise Inc., Beijing, 100080, China.
  • Cheng SH; Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medicine, Beijing, 100730, China.
  • Li J; Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medicine, Beijing, 100730, China.
  • Zhu L; Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medicine, Beijing, 100730, China.
  • Wu QL; Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medicine, Beijing, 100730, China.
  • Jin ZY; Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medicine, Beijing, 100730, China.
Zhonghua Yi Xue Za Zhi ; 101(7): 470-475, 2021 Feb 23.
Article em Zh | MEDLINE | ID: mdl-33631890
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: Zh Revista: Zhonghua Yi Xue Za Zhi Ano de publicação: 2021 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: Zh Revista: Zhonghua Yi Xue Za Zhi Ano de publicação: 2021 Tipo de documento: Article