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Article: SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset

TitleSpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset
Authors
KUANG, XCheung, JPYDing, XWZhang, T
Issue Date2021
Citation
Medical Image Computing and Computer Assisted Intervention - MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12902. Springer, Cham, 2021, p. 145-154 How to Cite?
DOI: http://dx.doi.org/10.1007/978-3-030-87196-3_14
Persistent Identifierhttp://hdl.handle.net/10722/311312
ISI Accession Number ID
WOS:000712020700014

 

DC FieldValueLanguage
dc.contributor.authorKUANG, X-
dc.contributor.authorCheung, JPY-
dc.contributor.authorDing, XW-
dc.contributor.authorZhang, T-
dc.date.accessioned2022-03-21T08:47:54Z-
dc.date.available2022-03-21T08:47:54Z-
dc.date.issued2021-
dc.identifier.citationMedical Image Computing and Computer Assisted Intervention - MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12902. Springer, Cham, 2021, p. 145-154-
dc.identifier.urihttp://hdl.handle.net/10722/311312-
dc.languageeng-
dc.relation.ispartofMedical Image Computing and Computer Assisted Intervention - MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12902. Springer, Cham-
dc.titleSpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset-
dc.typeArticle-
dc.identifier.emailCheung, JPY: cheungjp@hku.hk-
dc.identifier.emailZhang, T: tgzhang@hku.hk-
dc.identifier.authorityCheung, JPY=rp01685-
dc.identifier.authorityZhang, T=rp02821-
dc.identifier.doi10.1007/978-3-030-87196-3_14-
dc.identifier.hkuros332153-
dc.identifier.spage145-
dc.identifier.epage154-
dc.identifier.isiWOS:000712020700014-

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