Augmentation Technique For Artificial Phase-Contrast Microscopy Images Generation For The Training Of Deep Learning Algorithms

but.event.date25.04.2019cs
but.event.titleStudent EEICT 2019cs
dc.contributor.authorMívalt, Filip
dc.date.accessioned2020-04-16T07:19:30Z
dc.date.available2020-04-16T07:19:30Z
dc.date.issued2019cs
dc.description.abstractPhase contrast segmentation is crucial for various biological tasks such us quantitative, comparative or single cell level analysis. The popularity of image segmentation using deep learning strategies has been transferred into the field of microscopy imaging as well. Since the huge amount of training data is usually required, the annotation is time-consuming and lengthy. This paper introduces the method and augmentation techniques for artificial phase-contrast images generation aiming at the training of deep learning algorithms.en
dc.formattextcs
dc.format.extent199-202cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings of the 25st Conference STUDENT EEICT 2019. s. 199-202. ISBN 978-80-214-5735-5cs
dc.identifier.isbn978-80-214-5735-5
dc.identifier.urihttp://hdl.handle.net/11012/186652
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 25st Conference STUDENT EEICT 2019en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectdeep learningen
dc.subjectphase-contrasten
dc.subjectcell segmentationen
dc.subjectdata augmentationen
dc.subjectartificial data generationen
dc.titleAugmentation Technique For Artificial Phase-Contrast Microscopy Images Generation For The Training Of Deep Learning Algorithmsen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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