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Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics
In radiomics, utilizing features extracted from pretrained deep networks could result in models with a higher predictive performance than those relying on hand-crafted features. This study compared the predictive performance of models trained with either deep features, hand-crafted features, or a co...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606594/ https://www.ncbi.nlm.nih.gov/pubmed/37892087 http://dx.doi.org/10.3390/diagnostics13203266 |