Continuous learning AI in radiology: implementation principles and early applications
Continuous learning artificial intelligence algorithms learn and retrain continually, making them less prone to error and bias.
Gespeichert in:
| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
October 2020
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| In: |
Radiology
Year: 2020, Jahrgang: 297, Heft: 1, Pages: 6-14 |
| ISSN: | 1527-1315 |
| DOI: | 10.1148/radiol.2020200038 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1148/radiol.2020200038 Verlag, lizenzpflichtig, Volltext: https://pubs.rsna.org/doi/10.1148/radiol.2020200038 
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| Verfasserangaben: | Oleg S. Pianykh, Georg Langs, Marc Dewey, Dieter R. Enzmann, Christian J. Herold, Stefan O. Schoenberg, James A. Brink |
MARC
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