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Prostatic urinary tract visualization with super-resolution deep learning models.
Yoshimura, Takaaki; Nishioka, Kentaro; Hashimoto, Takayuki; Mori, Takashi; Kogame, Shoki; Seki, Kazuya; Sugimori, Hiroyuki; Yamashina, Hiroko; Nomura, Yusuke; Kato, Fumi; Kudo, Kohsuke; Shimizu, Shinichi; Aoyama, Hidefumi.
Afiliação
  • Yoshimura T; Department of Health Sciences and Technology, Faculty of Health Sciences, Hokkaido University, Sapporo, Japan.
  • Nishioka K; Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan.
  • Hashimoto T; Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
  • Mori T; Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
  • Kogame S; Department of Radiation Oncology, Hokkaido University Hospital, Department of Radiation Oncology, Hokkaido University Hospital, Sapporo, Japan.
  • Seki K; Division of Radiological Science and Technology, Department of Health Sciences, School of Medicine, Hokkaido University, Sapporo, Japan.
  • Sugimori H; Division of Radiological Science and Technology, Department of Health Sciences, School of Medicine, Hokkaido University, Sapporo, Japan.
  • Yamashina H; Department of Biomedical Science and Engineering, Faculty of Health Sciences, Hokkaido University, Sapporo, Japan.
  • Nomura Y; Clinical AI Human Resources Development Program, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
  • Kato F; Department of Biomedical Science and Engineering, Faculty of Health Sciences, Hokkaido University, Sapporo, Japan.
  • Kudo K; Department of Radiation oncology, Stanford University, Stanford, CA, United States of America.
  • Shimizu S; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
  • Aoyama H; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan.
PLoS One ; 18(1): e0280076, 2023.
Article em En | MEDLINE | ID: mdl-36607999
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans / Male Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans / Male Idioma: En Ano de publicação: 2023 Tipo de documento: Article