Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT
To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2024
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| In: |
European radiology
Year: 2024, Jahrgang: 34, Heft: 7, Pages: 4379–4392 |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-023-10540-3 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00330-023-10540-3
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| Verfasserangaben: | Silvia D. Almeida, Tobias Norajitra, Carsten T. Lüth, Tassilo Wald, Vivienn Weru, Marco Nolden, Paul F. Jäger, Oyunbileg von Stackelberg, Claus Peter Heußel, Oliver Weinheimer, Jürgen Biederer, Hans-Ulrich Kauczor and Klaus Maier-Hein |
MARC
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