dc.contributor.author | Lundervold, Alexander Selvikvåg | |
dc.contributor.author | Lundervold, Arvid | |
dc.date.accessioned | 2019-01-03T08:01:28Z | |
dc.date.available | 2019-01-03T08:01:28Z | |
dc.date.created | 2018-12-13T17:21:02Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 0939-3889 | |
dc.identifier.uri | http://hdl.handle.net/11250/2578841 | |
dc.description.abstract | What 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. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Elsevier | nb_NO |
dc.relation.uri | https://mmiv.no | |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.subject | machine learning | nb_NO |
dc.subject | deep learning | nb_NO |
dc.subject | medical imaging | nb_NO |
dc.subject | MRI | nb_NO |
dc.title | An overview of deep learning in medical imaging focusing on MRI | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.rights.holder | © authors | nb_NO |
dc.subject.nsi | VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429 | nb_NO |
dc.subject.nsi | VDP::Teknologi: 500::Medisinsk teknologi: 620 | nb_NO |
dc.source.pagenumber | 26 | nb_NO |
dc.source.journal | Zeitschrift für Medizinische Physik | nb_NO |
dc.identifier.doi | 10.1016/j.zemedi.2018.11.002 | |
dc.identifier.cristin | 1642994 | |
dc.relation.project | Bergens forskningsstiftelse: BFS2017TMT06 | nb_NO |
cristin.unitcode | 203,2,30,0 | |
cristin.unitname | Institutt for data- og realfag - Bergen | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |