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Prediction of reader estimates of mammographic density using convolutional neural networks.
Ionescu, Georgia V; Fergie, Martin; Berks, Michael; Harkness, Elaine F; Hulleman, Johan; Brentnall, Adam R; Cuzick, Jack; Evans, D Gareth; Astley, Susan M.
Afiliação
  • Ionescu GV; University of Manchester, School of Computer Science, Manchester, United Kingdom.
  • Fergie M; University of Manchester, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom.
  • Berks M; University of Manchester, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom.
  • Harkness EF; University of Manchester, School of Biological Sciences, Division of Neuroscience and Experimental Psychology, Manchester, United Kingdom.
  • Hulleman J; University of Manchester, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom.
  • Brentnall AR; University of Manchester, Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.
  • Cuzick J; Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Prevent Breast Cancer and Nightingale Breast Screening Centre, Wythenshawe, Manchester, United Kingdom.
  • Evans DG; University of Manchester, School of Biological Sciences, Division of Neuroscience and Experimental Psychology, Manchester, United Kingdom.
  • Astley SM; Queen Mary University of London, Wolfson Institute of Preventive Medicine, Centre for Cancer Prevention, London, United Kingdom.
J Med Imaging (Bellingham) ; 6(3): 031405, 2019 Jul.
Article em En | MEDLINE | ID: mdl-30746393
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Med Imaging (Bellingham) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Estados Unidos
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: J Med Imaging (Bellingham) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Estados Unidos