Optimizing the cervix cytological examination based on deep learning and dynamic shape modelling

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Open Access

Field	Value	Language
dc.contributor.author	Tareef, Afaf
dc.contributor.author	Song, Yang
dc.contributor.author	Huang, Heng
dc.contributor.author	Wang, Yue
dc.contributor.author	Feng, Dagan
dc.contributor.author	Chen, Mei
dc.contributor.author	Cai, Weidong
dc.date.accessioned	2019-12-18
dc.date.available	2019-12-18
dc.date.issued	2017-03-08
dc.identifier.citation	A Tareef, Y Song, H Huang, Y Wang, D Feng, M Chen, W Cai, “Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling”, Neurocomputing, 248:28-40, July 2017. https://doi.org/10.1016/j.neucom.2017.01.093	en
dc.identifier.issn	0925-2312
dc.identifier.uri	https://hdl.handle.net/2123/21555
dc.publisher	Elsevier	en
dc.relation	ARC DE150101655	en
dc.rights	Other	en
dc.subject	Overlapping cell segmentation, Convolutional neural network, Feature learning, Sparse approximation, Level set evolution	en
dc.title	Optimizing the cervix cytological examination based on deep learning and dynamic shape modelling	en
dc.type	Article	en
dc.identifier.doi	10.1016/j.neucom.2017.01.093	en
dc.type.pubtype	Preprint	en
dc.rights.other	© 2017 Elsevier B.V.The final authenticated version is available online at: https://doi.org/10.1016/j.neucom.2017.01.093 with CC-BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/	en
usyd.faculty	Faculty of Engineering	en