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Learning from scanners: Bias reduction and feature correction in radiomics
PURPOSE: Radiomics are quantitative features extracted from medical images. Many radiomic features depend not only on tumor properties, but also on non-tumor related factors such as scanner signal-to-noise ratio (SNR), reconstruction kernel and other image acquisition settings. This causes undesirab...
Gespeichert in:
| Veröffentlicht in: | Clin Transl Radiat Oncol |
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| Hauptverfasser: | , , , , , , , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Elsevier
2019
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6690665/ https://ncbi.nlm.nih.gov/pubmed/31417963 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.ctro.2019.07.003 |
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