ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation.
| Published in: | Medical & Biological Engineering & Computing Vol. 62; no. 6; pp. 1673 - 1688 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jun2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177078973&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177078973 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2024 vid: 62 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177078973 175292810 10.1007/s11517-024-03025-y 177078973 ppf: 1673 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation. aug: au: Ji, Zhanlin Mu, Juncheng Liu, Jianuo Zhang, Haiyang Dai, Chenxu Zhang, Xueji Ganchev, Ivan affil: https://ror.org/04z4wmb81 Department of Artificial Intelligence, North China University of Science and Technology, 063009, Tangshan, People's Republic of China sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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