7 citations · 20 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…