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Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study.
Wennmann, Markus; Klein, André; Bauer, Fabian; Chmelik, Jiri; Grözinger, Martin; Uhlenbrock, Charlotte; Lochner, Jakob; Nonnenmacher, Tobias; Rotkopf, Lukas Thomas; Sauer, Sandra; Hielscher, Thomas; Götz, Michael; Floca, Ralf Omar; Neher, Peter; Bonekamp, David; Hillengass, Jens; Kleesiek, Jens; Weinhold, Niels; Weber, Tim Frederik; Goldschmidt, Hartmut; Delorme, Stefan; Maier-Hein, Klaus; Schlemmer, Heinz-Peter.
Afiliação
  • Wennmann M; From the Divisions of Radiology.
  • Klein A; Medical Image Computing, German Cancer Research Center.
  • Grözinger M; From the Divisions of Radiology.
  • Lochner J; From the Divisions of Radiology.
  • Nonnenmacher T; Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg.
  • Rotkopf LT; From the Divisions of Radiology.
  • Sauer S; Department of Medicine V, Multiple Myeloma Section, University Hospital Heidelberg.
  • Hielscher T; Division of Biostatistics, German Cancer Research Center, Heidelberg.
  • Neher P; Medical Image Computing, German Cancer Research Center.
  • Bonekamp D; From the Divisions of Radiology.
  • Hillengass J; Department of Medicine, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY.
  • Weinhold N; Department of Medicine V, Multiple Myeloma Section, University Hospital Heidelberg.
  • Weber TF; Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg.
  • Delorme S; From the Divisions of Radiology.
Invest Radiol ; 57(11): 752-763, 2022 Nov 01.
Article em En | MEDLINE | ID: mdl-35640004
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Neoplasias Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Invest Radiol 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: Aprendizado Profundo / Neoplasias Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Invest Radiol Ano de publicação: 2022 Tipo de documento: Article