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dc.contributor.authorBorgli, Hanna
dc.contributor.authorThambawita, Vajira
dc.contributor.authorSmedsrud, Pia H
dc.contributor.authorHicks, Steven
dc.contributor.authorJha, Debesh
dc.contributor.authorEskeland, Sigrun Losada
dc.contributor.authorRandel, Kristin Ranheim
dc.contributor.authorPogorelov, Konstantin
dc.contributor.authorLux, Mathias
dc.contributor.authorDang Nguyen, Duc Tien
dc.contributor.authorJohansen, Dag
dc.contributor.authorGriwodz, Carsten
dc.contributor.authorStensland, Håkon Kvale
dc.contributor.authorGarcia-Ceja, Enrique
dc.contributor.authorSchmidt, Peter T
dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorRiegler, Michael
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorde Lange, Thomas
dc.description.abstractArtificial 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.en_US
dc.publisherNature Researchen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleHyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopyen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.rights.holderCopyright 2020 The Author(s).en_US
dc.source.journalScientific Dataen_US
dc.identifier.citationScientific Data. 7, 283en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal