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dc.contributor.advisorLangø, Thomas
dc.contributor.advisorSørbye, Sigrunn Holbek
dc.contributor.advisorValla, Marit
dc.contributor.advisorSorger, Hanne
dc.contributor.authorPedersen, André
dc.date.accessioned2020-10-26T13:28:37Z
dc.date.available2020-10-26T13:28:37Z
dc.date.issued2019-06-01
dc.description.abstractIn 2018, cancer was the second leading cause of death worldwide. Early detection can reduce mortality. Screening programs intended for early detection increases the workload for clinicians. To improve efficiency CAD systems would be highly beneficial. We have developed CAD systems using deep learning, for automatic tissue segmentation and prediction of diagnosis in lung and breast cancer. The first subproject focuses on automatic detection, 3D segmentation and malignancy prediction of lung nodules in CT, and the other aims to design an automatic method for breast tumor segmentation and histological grade prediction. For lung nodule segmentation, we designed a new 3D-UNet architecture to handle larger input chunks than what is commonly used. Our best model achieved 0.915 recall, 2.9 FPR and 0.813 DSCTP on a subset of the LIDC data set. For malignancy prediction we designed a CNN architecture that achieved a weighted average f1-score of 0.960, only requiring a centroid initialization of the nodule. We then designed an autoencoder for breast tumor segmentation, and achieved a DSC of 0.895 and 0.881 on two independent data sets. For histological grade prediction, we achieved a weighted average f1-score of 0.824. Using max voting we produced correct classification of 10/12 WSIs.en_US
dc.identifier.urihttps://hdl.handle.net/10037/19673
dc.language.isoengen_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.courseIDSTA-3941
dc.subjectDeep Learningen_US
dc.subjectComputer-aided diagnosis systemen_US
dc.subjectCancer analysisen_US
dc.subjectMachine visionen_US
dc.subjectMedical imagingen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectVDP::Medical disciplines: 700::Clinical medical disciplines: 750::Oncology: 762en_US
dc.subjectVDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Onkologi: 762en_US
dc.subjectVDP::Technology: 500::Medical technology: 620en_US
dc.subjectVDP::Teknologi: 500::Medisinsk teknologi: 620en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation, visualization, signal processing, image processing: 429en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429en_US
dc.titleA Step Towards Deep Learning-based CADs for Cancer Analysis in Medical Imagingen_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)