3 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis
Nikhil J. Dhinagar, Amit Singh, Saket Ozarkar +11
Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning invol…
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…