GIFNet: an effective global infection feature network for automatic COVID-19 lung lesions segmentation.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 63; no. 8; pp. 2463 - 2485 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187120523&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187120523 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2025 vid: 63 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187120523 175226030 10.1007/s11517-024-03024-z 187120523 ppf: 2463 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: GIFNet: an effective global infection feature network for automatic COVID-19 lung lesions segmentation. aug: au: Murmu, Anita Kumar, Piyush affil: https://ror.org/056wyhh33 Computer Science and Engineering Department, National Institute of Technology Patna, Ashok Rajpath, 800005, Patna, Bihar, India sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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