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dc.contributor.authorJha, Debesh
dc.contributor.authorAli, Sharib
dc.contributor.authorHicks, Steven
dc.contributor.authorThambawita, Vajira L B
dc.contributor.authorBorgli, Hanna
dc.contributor.authorSmedsrud, Pia H.
dc.contributor.authorde Lange, Thomas
dc.contributor.authorPogorelov, Konstantin
dc.contributor.authorWang, Xiaowei
dc.contributor.authorHarzig, Philipp
dc.contributor.authorTran, Minh-Triet
dc.contributor.authorMeng, Wenhua
dc.contributor.authorHoang, Trung-Hieu
dc.contributor.authorDias, Danielle
dc.contributor.authorKo, Tobey H.
dc.contributor.authorAgrawal, Taruna
dc.contributor.authorOstroukhova, Olga
dc.contributor.authorKhan, Zeshan
dc.contributor.authorTahir, Muhammed Atif
dc.contributor.authorLiu, Yang
dc.contributor.authorChang, Yuan
dc.contributor.authorKirkerød, Mathias
dc.contributor.authorJohansen, Dag
dc.contributor.authorLux, Mathias
dc.contributor.authorJohansen, Håvard D.
dc.contributor.authorRiegler, Michael
dc.contributor.authorHalvorsen, Pål
dc.date.accessioned2021-12-22T11:27:10Z
dc.date.available2021-12-22T11:27:10Z
dc.date.issued2021-02-19
dc.description.abstractGastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.en_US
dc.identifier.citationJha, Ali, Hicks, Thambawita, Borgli, Smedsrud, de Lange, Pogorelov, Wang, Harzig, Tran, Meng, Hoang, Dias, Ko, Agrawal, Ostroukhova, Khan, Tahir, Liu, Chang, Kirkerød, Johansen, Lux, Johansen, Riegler, Halvorsen. A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging. Medical Image Analysis. 2021;70en_US
dc.identifier.cristinIDFRIDAID 1917438
dc.identifier.doi10.1016/j.media.2021.102007
dc.identifier.issn1361-8415
dc.identifier.issn1361-8423
dc.identifier.urihttps://hdl.handle.net/10037/23476
dc.language.isoengen_US
dc.publisherElsevieren_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.journalMedical Image Analysis
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKTPLUSS/263248/Norway/Protecting Shared Data with Privacy Automatons//en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/BIA/282315/Norway/AutoCap: Automatic Anomaly Detection in Video Capsule Endoscopy//en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.titleA comprehensive analysis of classification methods in gastrointestinal endoscopy imagingen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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