Secure neuroimaging analysis using federated learning with homomorphic encryption. Stripelis, D., Saleem, H., Ghai, T., Dhinagar, N., Gupta, U., Anastasiou, C., Steeg, G. V., Ravi, S., Naveed, M., Thompson, P. M., & Ambite, J. L. In Rittner, L., M.D., E. R. C., Lepore, N., Brieva, J., & Linguraru, M. G., editors, 17th International Symposium on Medical Information Processing and Analysis, volume 12088, pages 351 – 359, 2021. International Society for Optics and Photonics, SPIE.
Secure neuroimaging analysis using federated learning with homomorphic encryption [link]Paper  doi  bibtex   
@inproceedings{10.1117/12.2606256,
author = {Dimitris Stripelis and Hamza Saleem and Tanmay Ghai and Nikhil Dhinagar and Umang Gupta and Chrysovalantis Anastasiou and Greg Ver Steeg and Srivatsan Ravi and Muhammad Naveed and Paul M. Thompson and Jose Luis Ambite},
title = {{Secure neuroimaging analysis using federated learning with homomorphic encryption}},
volume = {12088},
booktitle = {17th International Symposium on Medical Information Processing and Analysis},
editor = {Letícia Rittner and Eduardo Romero Castro M.D. and Natasha Lepore and Jorge Brieva and Marius George Linguraru},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {351 -- 359},
keywords = {federated learning, homomorphic encryption, secure computation, privacy-preserving neuroimaging analysis, MRI, brain age, deep learning, privacy},
year = {2021},
doi = {10.1117/12.2606256},
URL = {https://doi.org/10.1117/12.2606256}
}

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