Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality.

Wang, Yuqi; Li, Xiang; Konanur, Meghana; Konkel, Brandon; Seyferth, Elisabeth; Brajer, Nathan; Liu, Jian-Guo; Bashir, Mustafa R; Lafata, Kyle J

Wang, Yuqi; Li, Xiang; Konanur, Meghana; Konkel, Brandon; Seyferth, Elisabeth; Brajer, Nathan; Liu, Jian-Guo; Bashir, Mustafa R; Lafata, Kyle J.

Afiliação

Wang Y; Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.
Li X; Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.
Konanur M; Department of Radiology, Duke University, Durham, North Carolina, USA.
Konkel B; Department of Radiology, Duke University, Durham, North Carolina, USA.
Seyferth E; Department of Radiology, Duke University, Durham, North Carolina, USA.
Brajer N; Department of Radiology, Duke University, Durham, North Carolina, USA.
Liu JG; Department of Mathematics, Duke University, Durham, North Carolina, USA.
Bashir MR; Department of Physics, Duke University, Durham, North Carolina, USA.
Lafata KJ; Department of Radiology, Duke University, Durham, North Carolina, USA.

Med Phys ; 50(6): 3526-3537, 2023 Jun.

Article em En | MEDLINE | ID: mdl-36548913

Assuntos

Hipertensão; Redes Neurais de Computação; Humanos; Tomografia Computadorizada por Raios X/métodos; Radiografia Abdominal; Fígado

Palavras-chave

data fusion; deep learning; imaging; radiomics

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Hipertensão Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Med Phys Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

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