Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals (“privacy-preserving” distribut...

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Detalles Bibliográficos
Autores principales: Bogowicz, Marta, Jochems, Arthur, Deist, Timo M., Tanadini-Lang, Stephanie, Huang, Shao Hui, Chan, Biu, Waldron, John N., Bratman, Scott, O’Sullivan, Brian, Riesterer, Oliver, Studer, Gabriela, Unkelbach, Jan, Barakat, Samir, Brakenhoff, Ruud H., Nauta, Irene, Gazzani, Silvia E., Calareso, Giuseppina, Scheckenbach, Kathrin, Hoebers, Frank, Wesseling, Frederik W. R., Keek, Simon, Sanduleanu, Sebastian, Leijenaar, Ralph T. H., Vergeer, Marije R., Leemans, C. René, Terhaard, Chris H. J., van den Brekel, Michiel W. M., Hamming-Vrieze, Olga, van der Heijden, Martijn A., Elhalawani, Hesham M., Fuller, Clifton D., Guckenberger, Matthias, Lambin, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066122/
https://www.ncbi.nlm.nih.gov/pubmed/32161279
http://dx.doi.org/10.1038/s41598-020-61297-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066122/
https://www.ncbi.nlm.nih.gov/pubmed/32161279
http://dx.doi.org/10.1038/s41598-020-61297-4

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