Federated learning enables big data for rare cancer boundary detection

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...

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Egile Nagusiak:	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
Formatua:	Journal article
Hizkuntza:	English
Argitaratua:	Springer Nature 2022

Federated learning enables big data for rare cancer boundary detection

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