Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics
Aydin Demircioğlu · 2023 · Diagnostics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
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…
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Metadata source: Crossref · DOI 10.3390/diagnostics13203266