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dc.contributor.authorLundervold, Alexander Selvikvågen_US
dc.contributor.authorLundervold, Arviden_US
dc.PublishedLundervold A.S., Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik. 2018:10775eng
dc.description.abstractWhat has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.en_US
dc.rightsAttribution CC BYeng
dc.subjectMachine learningeng
dc.subjectDeep learningeng
dc.subjectMedical imagingeng
dc.titleAn overview of deep learning in medical imaging focusing on MRIen_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright 2018 The Authors
dc.source.journalZeitschrift für Medizinische Physik
dc.relation.projectBergens forskningsstiftelse: BFS2017TMT06

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