Your browser doesn't support javascript.
loading
Deep learning prediction model for central lymph node metastasis in papillary thyroid microcarcinoma based on cytology.
Ren, Wenhao; Zhu, Yanli; Wang, Qian; Song, Yuntao; Fan, Zhihui; Bai, Yanhua; Lin, Dongmei.
Afiliação
  • Ren W; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Zhu Y; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Wang Q; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Song Y; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Head and Neck Surgery, Peking University Cancer Hospital and Institute, Beijing, China.
  • Fan Z; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Ultrasound, Peking University Cancer Hospital and Institute, Beijing, China.
  • Bai Y; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Lin D; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China.
Cancer Sci ; 114(10): 4114-4124, 2023 Oct.
Article em En | MEDLINE | ID: mdl-37574759
Palavras-chave
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancer Sci Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancer Sci Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China