Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images.
| Publicado en: | Japanese Journal of Radiology Vol. 43; no. 11; pp. 1842 - 1849 |
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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=189002602&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189002602 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18671071 AUCM jtl: Japanese Journal of Radiology issn: 18671071 maglogo: N pubinfo: dt: Nov2025 vid: 43 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189002602 186436091 10.1007/s11604-025-01828-z 189002602 ppf: 1842 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images. aug: au: Higashibori, Haruka Fukumoto, Wataru Kusuda, Sayaka Yokomachi, Kazushi Mitani, Hidenori Nakamura, Yuko Awai, Kazuo affil: https://ror.org/03t78wx29 Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, 734-8551, Minamiku, Hiroshima, Japan sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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