Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients.
| Publicado en: | European Radiology Vol. 34; no. 3; pp. 2036 - 2048 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2024
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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=175530218&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175530218 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Mar2024 vid: 34 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175530218 10.1007/s00330-023-10135-y 175530218 ppf: 2036 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients. aug: au: Lee, Heejin Chang, Won Kim, Hae Young Sung, Pamela Cho, Jungheum Lee, Yoon Jin Kim, Young Hoon affil: https://ror.org/04h9pn542 Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Gyeonggi-do, Suwon-si, Republic of Korea sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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