Leveraging deep learning-based kernel conversion for more precise airway quantification on CT.
| Publicado en: | European Radiology Vol. 35; no. 11; pp. 7185 - 7199 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2025
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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=188901913&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188901913 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Nov2025 vid: 35 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188901913 185334488 10.1007/s00330-025-11696-w 188901913 ppf: 7185 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Leveraging deep learning-based kernel conversion for more precise airway quantification on CT. aug: au: Choe, Jooae Yun, Jihye Kim, Myeong Jun Oh, Yu Jin Bae, Seungbin Yu, Donghoon Seo, Joon Beom Lee, Sang Min Lee, Ho Yun affil: https://ror.org/03s5q0090 Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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