7 citations · 20 across the 11 of their papers we have counts for
13 papers
Incomplete Multimodal Learning for Complex Brain Disorders Prediction
Reza Shirkavand, Liang Zhan, Heng Huang +2
Recent advancements in the acquisition of various brain data sources have created new opportunities for integrating multimodal brain data to assist in early detection of complex br…
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI
Nikhil J. Dhinagar, Vignesh Santhalingam, Katherine E. Lawrence +2
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions…
Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection
Nikhil J. Dhinagar, Sophia I. Thomopoulos, Emily Laltoo +1
Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such a…
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models
Umang Gupta, Tamoghna Chattopadhyay, Nikhil Dhinagar +3
Transfer learning has remarkably improved computer vision. These advances also promise improvements in neuroimaging, where training set sizes are often small. However, various diff…
Curriculum Based Multi-Task Learning for Parkinson's Disease Detection
Nikhil J. Dhinagar, Conor Owens-Walton, Emily Laltoo +9
There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodege…
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…
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Dimitris Stripelis, Hamza Saleem, Tanmay Ghai +8
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to…
Membership Inference Attacks on Deep Regression Models for Neuroimaging
Umang Gupta, Dimitris Stripelis, Pradeep K. Lam +3
Ensuring the privacy of research participants is vital, even more so in healthcare environments. Deep learning approaches to neuroimaging require large datasets, and this often nec…
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…
Improved Brain Age Estimation with Slice-based Set Networks
Umang Gupta, Pradeep K. Lam, Greg Ver Steeg +1
Deep Learning for neuroimaging data is a promising but challenging direction. The high dimensionality of 3D MRI scans makes this endeavor compute and data-intensive. Most conventio…
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…