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dc.contributor.advisorJenssen, Robert
dc.contributor.advisorKampffmeyer, Michael
dc.contributor.authorKaspersen, Oskar Anstein
dc.date.accessioned2020-01-06T08:49:31Z
dc.date.available2020-01-06T08:49:31Z
dc.date.issued2019-10-31
dc.description.abstractIn this thesis, we look at a deep learning approach to AD detection and focus specifically on the problem of class imbalance, which arises from the fact that lesions only occupy a small part of the images, by analyzing how weighting of the loss function can help address this issue. Balancing the weights of the foreground and background class in the cost-function was found to be crucial to achieve good segmentation results.en_US
dc.identifier.urihttps://hdl.handle.net/10037/17022
dc.language.isonoben_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2019 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDFYS-3921
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410en_US
dc.subjectVDP::Technology: 500::Electrotechnical disciplines: 540en_US
dc.subjectVDP::Teknologi: 500::Elektrotekniske fag: 540en_US
dc.titleInvestigating the effect of class-imbalance on convolutional neural networks for angiodysplasia detectionen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)