NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data
Author:
Publisher
Springer Science and Business Media LLC
Subject
Information Systems,General Neuroscience,Software
Link
https://link.springer.com/content/pdf/10.1007/s12021-021-09525-8.pdf
Reference74 articles.
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3. Aine, C.J., Bockholt, H.J., Bustillo, J.R., Cañive, J.M., Caprihan, A., Gasparovic, C., Hanlon, F.M., Houck, J.M, Jung, R.E, Lauriello, J., et al. (2017). Multimodal neuroimaging in schizophrenia: description and dissemination. Neuroinformatics, 15(4), 343–364.
4. Arbabshirani, M.R., Plis, S., Sui, J., Calhoun, V.D. (2017). Single subject prediction of brain disorders in neuroimaging Promises and pitfalls. NeuroImage, 145(nil), 137–165. https://doi.org/10.1016/j.neuroimage.2016.02.079.
5. Baker, B., Abrol, A., Silva, R.F., Damaraju, E., Sarwate, A.D., Calhoun, V.D., Plis, S.M. (2019). Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings. NeuroImage, 186, 557–569. https://doi.org/10.1016/j.neuroimage.2018.10.072.
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