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dc.contributor.authorHenriksen, Fredrik Lund
dc.contributor.authorJensen, Rune
dc.contributor.authorStensland, Håkon Kvale
dc.contributor.authorJohansen, Dag
dc.contributor.authorRiegler, Michael
dc.contributor.authorHalvorsen, Pål
dc.date.accessioned2020-02-05T07:58:17Z
dc.date.available2020-02-05T07:58:17Z
dc.date.issued2019-08-05
dc.description.abstractDeep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract. Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells. Detection and removal of polyps can therefore prevent the development of cancer. Due to high cost, time consumption, patient discomfort and in-accuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures. Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate. We also experiment with how various training techniques can be used to increase performance. Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no, and even a negative, effect.en_US
dc.identifier.citationHenriksen, Jensen, Stensland H, Johansen D, Riegler M, Halvorsen P. Performance of data enhancements and training optimization for neural network: A polyp detection case study. IEEE International Symposium on Computer-Based Medical Systems. 2019en_US
dc.identifier.cristinIDFRIDAID 1738613
dc.identifier.doi10.1109/CBMS.2019.00067
dc.identifier.issn2372-9198
dc.identifier.urihttps://hdl.handle.net/10037/17323
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE International Symposium on Computer-Based Medical Systems
dc.rights.accessRightsopenAccessen_US
dc.rights.holder©2019 IEEEen_US
dc.subjectVDP::Medisinske Fag: 700en_US
dc.titlePerformance of data enhancements and training optimization for neural network: A polyp detection case studyen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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