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MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.
Graham, Simon; Chen, Hao; Gamper, Jevgenij; Dou, Qi; Heng, Pheng-Ann; Snead, David; Tsang, Yee Wah; Rajpoot, Nasir.
Afiliación
  • Graham S; Mathematics for Real World Systems Centre for Doctoral Training, University of Warwick, Coventry, CV4 7AL, UK; Department of Computer Science, University of Warwick, UK. Electronic address: s.graham.1@warwick.ac.uk.
  • Chen H; Department of Computer Science and Engineering, The Chinese University of Hong Kong, China.
  • Gamper J; Mathematics for Real World Systems Centre for Doctoral Training, University of Warwick, Coventry, CV4 7AL, UK; Department of Computer Science, University of Warwick, UK.
  • Dou Q; Department of Computer Science and Engineering, The Chinese University of Hong Kong, China.
  • Heng PA; Department of Computer Science and Engineering, The Chinese University of Hong Kong, China.
  • Snead D; Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK.
  • Tsang YW; Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK.
  • Rajpoot N; Department of Computer Science, University of Warwick, UK; Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK; The Alan Turing Institute, London, UK.
Med Image Anal ; 52: 199-211, 2019 02.
Article en En | MEDLINE | ID: mdl-30594772
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Neoplasias Colorrectales / Adenocarcinoma / Técnicas Histológicas / Aprendizaje Profundo Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2019 Tipo del documento: Article
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Neoplasias Colorrectales / Adenocarcinoma / Técnicas Histológicas / Aprendizaje Profundo Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2019 Tipo del documento: Article