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dc.contributor.authorChalupa, Daniel
dc.date.accessioned2020-05-07T09:40:29Z
dc.date.available2020-05-07T09:40:29Z
dc.date.issued2017cs
dc.identifier.citationProceedings of the 23st Conference STUDENT EEICT 2017. s. 296-298. ISBN 978-80-214-5496-5cs
dc.identifier.isbn978-80-214-5496-5
dc.identifier.urihttp://hdl.handle.net/11012/187112
dc.description.abstractThe purpose of this work is to introduce an extendable framework for training and usage of machine learning algorithms. This framework is bundled in an extension for 3D Slicer that is to be used for medical images segmentation. An example usage of the extension is also provided.en
dc.formattextcs
dc.format.extent296-298cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 23st Conference STUDENT EEICT 2017en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subject3D Sliceren
dc.subjectC++en
dc.subjectextensionen
dc.subjectmachine learningen
dc.subjectoptimizationen
dc.subjectsegmentationen
dc.subjecttomographyen
dc.titleSupervised Segmentation For 3D Sliceren
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date27.04.2017cs
but.event.titleStudent EEICT 2017cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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