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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.date.accessioned2021-01-23T19:28:59Z
dc.date.available2021-01-23T19:28:59Z
dc.date.issued2020-08-28
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 <i>HyperKvasir</i>, 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 <i>HyperKvasir</i> 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.identifier.citationBorgli H, Thambawita V, Smedsrud PH, Hicks S, Jha D, Eskeland SL, Randel KR, Pogorelov K, Lux M, Dang Nguyen DT, Johansen D, Griwodz C, Stensland H, Garcia-Ceja E, Schmidt PT, Hammer HL, Riegler M, Halvorsen P, de Lange. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Scientific Data. 2020en_US
dc.identifier.cristinIDFRIDAID 1833194
dc.identifier.doi10.1038/s41597-020-00622-y
dc.identifier.issn2052-4463
dc.identifier.urihttps://hdl.handle.net/10037/20442
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofJha, D. (2022). Machine Learning-based Classification, Detection, and Segmentation of Medical Images. (Doctoral thesis). <a href=https://hdl.handle.net/10037/23693>https://hdl.handle.net/10037/23693</a>.
dc.relation.journalScientific Data
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKTPLUSS/263248/Norway/Protecting Shared Data with Privacy Automatons/Privaton/en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/BIA/282315/Norway/Automatic Anomaly Detection in Video Capsule Endoscopy/AutoCap/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Medical disciplines: 700::Clinical medical disciplines: 750::Gastroenterology: 773en_US
dc.subjectVDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Gasteroenterologi: 773en_US
dc.subjectVDP::Technology: 500::Medical technology: 620en_US
dc.subjectVDP::Teknologi: 500::Medisinsk teknologi: 620en_US
dc.titleHyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopyen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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