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Self-supervised representation learning for surgical activity recognition.
Paysan, Daniel; Haug, Luis; Bajka, Michael; Oelhafen, Markus; Buhmann, Joachim M.
Affiliation
  • Paysan D; Department of Computer Science, ETH Zurich, Zurich, Switzerland. paysand@ethz.ch.
  • Haug L; Department of Computer Science, ETH Zurich, Zurich, Switzerland.
  • Bajka M; Division of Gynecology Department OB/GYN, University Hospital, Zurich, Switzerland.
  • Oelhafen M; VirtaMed AG, Schlieren, Switzerland.
  • Buhmann JM; Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Int J Comput Assist Radiol Surg ; 16(11): 2037-2044, 2021 Nov.
Article in En | MEDLINE | ID: mdl-34542839
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Supervised Machine Learning Type of study: Prognostic_studies Limits: Humans Language: En Journal: Int J Comput Assist Radiol Surg Journal subject: RADIOLOGIA Year: 2021 Document type: Article Affiliation country: Suiza Country of publication: Alemania
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Supervised Machine Learning Type of study: Prognostic_studies Limits: Humans Language: En Journal: Int J Comput Assist Radiol Surg Journal subject: RADIOLOGIA Year: 2021 Document type: Article Affiliation country: Suiza Country of publication: Alemania