Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.
| Published in: | European Radiology Vol. 33; no. 10; pp. 6689 - 6718 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Oct2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172041140&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172041140 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Oct2023 vid: 33 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 172041140 163654869 10.1007/s00330-023-09609-w 172041140 ppf: 6689 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. aug: au: Radiya, Keyur Joakimsen, Henrik Lykke Mikalsen, Karl Øyvind Aahlin, Eirik Kjus Lindsetmo, Rolv-Ole Mortensen, Kim Erlend affil: https://ror.org/030v5kp38 Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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