63 citations · 119 across the 41 of their papers we have counts for
6 papers · 1 filter
Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model
Wei Peng, Ehsan Adeli, Tomas Bosschieter +3
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by M…
Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome
Yueting Li, Qingyue Wei, Ehsan Adeli +2
The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental p…
Multiple Instance Neuroimage Transformer
Ayush Singla, Qingyu Zhao, Daniel K. Do +3
For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classif…
Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing
Magdalini Paschali, Qingyu Zhao, Ehsan Adeli +1
A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life even…
A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models
Anthony Vento, Qingyu Zhao, Robert Paul +2
Translating machine learning algorithms into clinical applications requires addressing challenges related to interpretability, such as accounting for the effect of confounding vari…
GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation
Mark Endo, Kathleen L. Poston, Edith V. Sullivan +3
Parkinson's disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture.…