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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...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6690665/ https://www.ncbi.nlm.nih.gov/pubmed/31417963 http://dx.doi.org/10.1016/j.ctro.2019.07.003 |