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Self-Distilled Supervised Contrastive Learning for diagnosis of breast cancers with histopathological images.
Gong, Ronglin; Wang, Linlin; Wang, Jun; Ge, Binjie; Yu, Hang; Shi, Jun.
Afiliação
  • Gong R; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information Engineering, Shanghai University, China; Shanghai Institute for Advanced Communication and Data
  • Wang L; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information Engineering, Shanghai University, China; Shanghai Institute for Advanced Communication and Data
  • Wang J; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information Engineering, Shanghai University, China; Shanghai Institute for Advanced Communication and Data
  • Ge B; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
  • Yu H; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
  • Shi J; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information Engineering, Shanghai University, China; Shanghai Institute for Advanced Communication and Data
Comput Biol Med ; 146: 105641, 2022 07.
Article em En | MEDLINE | ID: mdl-35617728
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Female / Humans Idioma: En Revista: Comput Biol Med 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 Assunto principal: Neoplasias da Mama Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Female / Humans Idioma: En Revista: Comput Biol Med Ano de publicação: 2022 Tipo de documento: Article