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Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model.
Taghiakbari, Mahsa; Hamidi Ghalehjegh, Sina; Jehanno, Emmanuel; Berthier, Tess; di Jorio, Lisa; Ghadakzadeh, Saber; Barkun, Alan; Takla, Mark; Bouin, Mickael; Deslandres, Eric; Bouchard, Simon; Sidani, Sacha; Bengio, Yoshua; von Renteln Md, Daniel.
Afiliación
  • Taghiakbari M; Faculty of Medicine, Department of Biomedical Sciences, University of Montreal, Montreal, Quebec, Canada.
  • Hamidi Ghalehjegh S; Department of Medicine, Division of Gastroenterology, University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada.
  • Jehanno E; Department of Artificial Intelligence, Imagia Canexia Health Inc., Montreal, Canada.
  • Berthier T; Department of Artificial Intelligence, Imagia Canexia Health Inc., Montreal, Canada.
  • di Jorio L; Department of Artificial Intelligence, Imagia Canexia Health Inc., Montreal, Canada.
  • Ghadakzadeh S; Department of Artificial Intelligence, Imagia Canexia Health Inc., Montreal, Canada.
  • Barkun A; Department of Artificial Intelligence, Imagia Canexia Health Inc., Montreal, Canada.
  • Takla M; Division of Gastroenterology, McGill University Health Center, McGill University, Montreal, Quebec, Canada.
  • Bouin M; Faculty of Medicine, Department of Biomedical Sciences, University of Montreal, Montreal, Quebec, Canada.
  • Deslandres E; Department of Medicine, Division of Gastroenterology, University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada.
  • Bouchard S; Department of Medicine, Division of Gastroenterology, University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada.
  • Sidani S; Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada.
  • Bengio Y; Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada.
  • von Renteln Md D; Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada.
J Can Assoc Gastroenterol ; 6(4): 145-151, 2023 Aug.
Article en En | MEDLINE | ID: mdl-37538187
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: J Can Assoc Gastroenterol Año: 2023 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: J Can Assoc Gastroenterol Año: 2023 Tipo del documento: Article