GL-RFE uses neural-network loss gradients for recursive feature elimination on 106 radiomic features, retaining the top 15 to reach 90.22% accuracy distinguishing early versus advanced lung cancer on CT scans.
Review of feature selection approaches based on grouping of features
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Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection
GL-RFE uses neural-network loss gradients for recursive feature elimination on 106 radiomic features, retaining the top 15 to reach 90.22% accuracy distinguishing early versus advanced lung cancer on CT scans.