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HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.
Borgli, Hanna; Thambawita, Vajira; Smedsrud, Pia H; Hicks, Steven; Jha, Debesh; Eskeland, Sigrun L; Randel, Kristin Ranheim; Pogorelov, Konstantin; Lux, Mathias; Nguyen, Duc Tien Dang; Johansen, Dag; Griwodz, Carsten; Stensland, Håkon K; Garcia-Ceja, Enrique; Schmidt, Peter T; Hammer, Hugo L; Riegler, Michael A; Halvorsen, Pål; de Lange, Thomas.
Afiliación
  • Borgli H; SimulaMet, Oslo, Norway.
  • Thambawita V; University of Oslo, Oslo, Norway.
  • Smedsrud PH; SimulaMet, Oslo, Norway.
  • Hicks S; Oslo Metropolitan University, Oslo, Norway.
  • Jha D; SimulaMet, Oslo, Norway.
  • Eskeland SL; University of Oslo, Oslo, Norway.
  • Randel KR; Augere Medical AS, Oslo, Norway.
  • Pogorelov K; SimulaMet, Oslo, Norway.
  • Lux M; Oslo Metropolitan University, Oslo, Norway.
  • Nguyen DTD; SimulaMet, Oslo, Norway.
  • Johansen D; UIT The Arctic University of Norway, Tromsø, Norway.
  • Griwodz C; Department of Medical Research, Bærum Hospital, Bærum, Norway.
  • Stensland HK; University of Oslo, Oslo, Norway.
  • Garcia-Ceja E; Cancer Registry of Norway, Oslo, Norway.
  • Schmidt PT; Simula Research Laboratory, Oslo, Norway.
  • Hammer HL; Klagenfurt University, Klagenfurt, Austria.
  • Riegler MA; University of Bergen, Bergen, Norway.
  • Halvorsen P; UIT The Arctic University of Norway, Tromsø, Norway.
  • de Lange T; University of Oslo, Oslo, Norway.
Sci Data ; 7(1): 283, 2020 08 28.
Article en En | MEDLINE | ID: mdl-32859981
ABSTRACT
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Endoscopía Gastrointestinal / Diagnóstico por Computador Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Sci Data Año: 2020 Tipo del documento: Article País de afiliación: Noruega

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Endoscopía Gastrointestinal / Diagnóstico por Computador Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Sci Data Año: 2020 Tipo del documento: Article País de afiliación: Noruega