7 citations · 20 across the 11 of their papers we have counts for
5 papers · 1 filter
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar +3
Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model prunin…
Functional2Structural: Cross-Modality Brain Networks Representation Learning
Haoteng Tang, Xiyao Fu, Lei Guo +7
MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, su…
Secure & Private Federated Neuroimaging
Dimitris Stripelis, Umang Gupta, Hamza Saleem +9
The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulator…
Scaling Neuroscience Research using Federated Learning
Dimitris Stripelis, Jose Luis Ambite, Pradeep Lam +1
The amount of biomedical data continues to grow rapidly. However, the ability to analyze these data is limited due to privacy and regulatory concerns. Machine learning approaches t…
Large-Scale Unsupervised Deep Representation Learning for Brain Structure
Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra +1
Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such wor…