BibTex format
@article{Pati:2023:10.1038/s41467-023-36188-7,
author = {Pati, S and Baid, U and Edwards, B and Sheller, M and Wang, S-H and Reina, GA and Foley, P and Gruzdev, A and Karkada, D and Davatzikos, C and Sako, C and Ghodasara, S and Bilello, M and Mohan, S and Vollmuth, P and Brugnara, G and Preetha, CJ and Sahm, F and Maier-Hein, K and Zenk, M and Bendszus, M and Wick, W and Calabrese, E and Rudie, J and Villanueva-Meyer, J and Cha, S and Ingalhalikar, M and Jadhav, M and Pandey, U and Saini, J and Garrett, J and Larson, M and Jeraj, R and Currie, S and Frood, R and Fatania, K and Huang, RY and Chang, K and Balaña, C and Capellades, J and Puig, J and Trenkler, J and Pichler, J and Necker, G and Haunschmidt, A and Meckel, S and Shukla, G and Liem, S and Alexander, GS and Lombardo, J and Palmer, JD and Flanders, AE and Dicker, AP and Sair, HI and Jones, CK and Venkataraman, A and Jiang, M and So, TY and Chen, C and Heng, PA and Dou, Q and Kozubek, M and Lux, F and Michálek, J and Matula, P and Kekovský, M and Kopivová, T and Dostál, M and Vybíhal},
doi = {10.1038/s41467-023-36188-7},
journal = {Nature Communications},
pages = {436--436},
title = {Author Correction: Federated learning enables big data for rare cancer boundary detection.},
url = {http://dx.doi.org/10.1038/s41467-023-36188-7},
volume = {14},
year = {2023}
}