Unsupervised machine learning identifies predictive progression markers of IPF.
| Published in: | European Radiology Vol. 33; no. 2; pp. 925 - 936 |
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| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Feb2023
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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=161607969&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161607969 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Feb2023 vid: 33 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161607969 158938914 10.1007/s00330-022-09101-x 161607969 ppf: 925 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Unsupervised machine learning identifies predictive progression markers of IPF. aug: au: Pan, Jeanny Hofmanninger, Johannes Nenning, Karl-Heinz Prayer, Florian Röhrich, Sebastian Sverzellati, Nicola Poletti, Venerino Tomassetti, Sara Weber, Michael Prosch, Helmut Langs, Georg affil: Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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