9 citations · 19 across the 5 of their papers we have counts for
6 papers
Self-Supervised Longitudinal Neighbourhood Embedding
Jiahong Ouyang, Qingyu Zhao, Ehsan Adeli +4
Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyzing this data via machine lear…
Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity
Mandy Lu, Kathleen Poston, Adolf Pfefferbaum +5
Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. Th…
Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs
Jiahong Ouyang, Qingyu Zhao, Edith V Sullivan +4
Many neurological diseases are characterized by gradual deterioration of brain structure and function. Large longitudinal MRI datasets have revealed such deterioration, in part, by…
Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis
Soham Gadgil, Qingyu Zhao, Adolf Pfefferbaum +3
The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep l…
Representation Learning with Statistical Independence to Mitigate Bias
Ehsan Adeli, Qingyu Zhao, Adolf Pfefferbaum +4
Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such…
Confounder-Aware Visualization of ConvNets
Qingyu Zhao, Ehsan Adeli, Adolf Pfefferbaum +2
With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better underst…