Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets
Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
November 01, 2022
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| In: |
Quantitative imaging in medicine and surgery
Year: 2022, Jahrgang: 12, Heft: 11, Pages: 4991-5003 |
| ISSN: | 2223-4306 |
| DOI: | 10.21037/qims-22-265 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21037/qims-22-265 Verlag, lizenzpflichtig, Volltext: https://qims.amegroups.org/article/view/101324 
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| Verfasserangaben: | Eva Gresser, Balthasar Schachtner, Anna Theresa Stüber, Olga Solyanik, Andrea Schreier, Thomas Huber, Matthias Frank Froelich, Giuseppe Magistro, Alexander Kretschmer, Christian Stief, Jens Ricke, Michael Ingrisch, Dominik Nörenberg |
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