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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Detalles Bibliográficos
Autor principal: Demircioğlu, Aydin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606594/
https://www.ncbi.nlm.nih.gov/pubmed/37892087
http://dx.doi.org/10.3390/diagnostics13203266

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