Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/20310
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dc.contributor.authorLei, Tao-
dc.contributor.authorZhou, Wenzheng-
dc.contributor.authorZhang, Yuxiao-
dc.contributor.authorWang, Risheng-
dc.contributor.authorMeng, Hongying-
dc.contributor.authorNandi, Asoke K.-
dc.coverage.spatialBacelona, Spain-
dc.date.accessioned2020-02-17T11:02:16Z-
dc.date.available2020-02-17T11:02:16Z-
dc.date.issued2020-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/20310-
dc.language.isoenen_US
dc.sourceICCASP 2020-
dc.sourceICCASP 2020-
dc.subjectdeep learningen_US
dc.subjectimage segmentationen_US
dc.subject3D fully convolutional neural networken_US
dc.subjectnetwork compressionen_US
dc.titleLightweight V-Net for Liver Segmentationen_US
dc.typeConference Paperen_US
pubs.finish-date2020-05-08-
pubs.finish-date2020-05-08-
pubs.publication-statusAccepted-
pubs.start-date2020-05-04-
pubs.start-date2020-05-04-
Appears in Collections:Dept of Electronic and Electrical Engineering Embargoed Research Papers

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