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A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment.
Zhu, Chen-Yu; Wang, Yu-Kun; Chen, Hai-Peng; Gao, Kun-Lun; Shu, Chang; Wang, Jun-Cheng; Yan, Li-Feng; Yang, Yi-Guang; Xie, Feng-Ying; Liu, Jie.
Afiliación
  • Zhu CY; Department of Dermatology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Wang YK; Department of Dermatology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Chen HP; DeepWise AI Lab, Beijing, China.
  • Gao KL; DeepWise AI Lab, Beijing, China.
  • Shu C; Department of Dermatology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Wang JC; Department of Dermatology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Yan LF; DeepWise AI Lab, Beijing, China.
  • Yang YG; Image Processing Center, School of Astronautics, Beihang University, Beijing, China.
  • Xie FY; Image Processing Center, School of Astronautics, Beihang University, Beijing, China.
  • Liu J; Department of Dermatology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Med (Lausanne) ; 8: 626369, 2021.
Article en En | MEDLINE | ID: mdl-33937279
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Front Med (Lausanne) Año: 2021 Tipo del documento: Article País de afiliación: China
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Front Med (Lausanne) Año: 2021 Tipo del documento: Article País de afiliación: China