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From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.
Yeung, Chris; Ungi, Tamas; Hu, Zoe; Jamzad, Amoon; Kaufmann, Martin; Walker, Ross; Merchant, Shaila; Engel, Cecil Jay; Jabs, Doris; Rudan, John; Mousavi, Parvin; Fichtinger, Gabor.
Afiliação
  • Yeung C; School of Computing, Queen's University, Kingston, ON, Canada. chris.yeung@queensu.ca.
  • Ungi T; School of Computing, Queen's University, Kingston, ON, Canada.
  • Hu Z; School of Medicine, Queen's University, Kingston, ON, Canada.
  • Jamzad A; School of Computing, Queen's University, Kingston, ON, Canada.
  • Kaufmann M; Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Walker R; Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Merchant S; Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Engel CJ; Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Jabs D; Department of Radiology, Queen's University, Kingston, ON, Canada.
  • Rudan J; Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Mousavi P; School of Computing, Queen's University, Kingston, ON, Canada.
  • Fichtinger G; School of Computing, Queen's University, Kingston, ON, Canada.
Int J Comput Assist Radiol Surg ; 19(6): 1193-1201, 2024 Jun.
Article em En | MEDLINE | ID: mdl-38642296
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Mastectomia Segmentar / Margens de Excisão / Aprendizado Profundo Limite: Female / Humans Idioma: En Revista: Int J Comput Assist Radiol Surg Assunto da revista: RADIOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Mastectomia Segmentar / Margens de Excisão / Aprendizado Profundo Limite: Female / Humans Idioma: En Revista: Int J Comput Assist Radiol Surg Assunto da revista: RADIOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá