A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lun...
A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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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 o...
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A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients
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TN_cdi_doaj_primary_oai_doaj_org_article_f46a4dfb4f9e4a9782e2fb980168130c
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_f46a4dfb4f9e4a9782e2fb980168130c
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-58241-1
