Federated learning enables big data for rare cancer boundary detection

Federated learning enables big data for rare cancer boundary detection

Veure altres versions (1)

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative...

Descripció completa

Dades bibliogràfiques
Autors principals: Pati, S, Baid, U, Edwards, B, Sheller, M, Wang, S-H, Reina, GA, Foley, P, Gruzdev, A, Karkada, D, Davatzikos, C, Sako, C, Ghodasara, S, Bilello, M, Mohan, S, Vollmuth, P, Brugnara, G, Preetha, CJ, Sahm, F, Maier-Hein, K, Zenk, M, Bendszus, M, Wick, W, Calabrese, E, Rudie, J, Villanueva-Meyer, J, Cha, S, Ingalhalikar, M, Jadhav, M, Pandey, U, Saini, J, Garrett, J, Larson, M, Jeraj, R, Currie, S, Frood, R, Fatania, K, Huang, RY, Chang, K, Balaña, C, Capellades, J, Puig, J, Trenkler, J, Pichler, J, Necker, G, Haunschmidt, A, Meckel, S, Shukla, G, Liem, S, Alexander, GS, Lombardo, J, Kamnitsas, K
Format: Journal article
Idioma:English
Publicat: Springer Nature 2022

Ítems similars