Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.
| Publicado en: | European Radiology Vol. 34; no. 11; pp. 7397 - 7408 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2024
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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=180550107&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180550107 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Nov2024 vid: 34 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 180550107 177389224 10.1007/s00330-024-10804-6 180550107 ppf: 7397 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review. aug: au: Wang, Ting-Wei Hong, Jia-Sheng Chiu, Hwa-Yen Chao, Heng-Sheng Chen, Yuh-Min Wu, Yu-Te affil: https://ror.org/00se2k293 Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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