A Distributed Feature Selection Pipeline for Survival Analysis using Radiomics in Non-Small Cell Lung Cancer Patients
Benedetta Gottardelli,
Varsha Gouthamchand,
Carlotta Masciocchi
et al.
Abstract:Predictive modelling of cancer outcomes using radiomics faces dimensionality problems and data limitations, as radiomics features often number in the hundreds, and multi-institutional data sharing is often unfeasible. Federated learning (FL) and feature selection (FS) techniques combined can help overcome these issues, as one provides the means of training models without exchanging sensitive data, while the other identifies the most informative features, reduces overfitting, and improves model interpretability… Show more
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