1 citations · 2 across the 7 of their papers we have counts for
7 papers
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs
Wei Peng, Tomas Bosschieter, Jiahong Ouyang +4
Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity. However, the mainstay of AI…
LSOR: Longitudinally-Consistent Self-Organized Representation Learning
Jiahong Ouyang, Qingyu Zhao, Ehsan Adeli +3
Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces ge…
Imputing Brain Measurements Across Data Sets via Graph Neural Networks
Yixin Wang, Wei Peng, Susan F. Tapert +2
Publicly available data sets of structural MRIs might not contain specific measurements of brain Regions of Interests (ROIs) that are important for training machine learning models…
An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment
Favour Nerrise, Qingyu Zhao, Kathleen L. Poston +2
One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying…
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…