11 citations · 43 across the 28 of their papers we have counts for
12 papers · 1 filter
Confounder-Free Continual Learning via Recursive Feature Normalization
Yash Shah, Camila Gonzalez, Mohammad H. Abbasi +3
Confounders are extraneous variables that affect both the input and the target, resulting in spurious correlations and biased predictions. There are recent advances in dealing with…
The Most Important Features in Generalized Additive Models Might Be Groups of Features
Tomas M. Bosschieter, Luis Franca, Jessica Wolk +8
While analyzing the importance of features has become ubiquitous in interpretable machine learning, the joint signal from a group of related features is sometimes overlooked or ina…
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…
Metadata Normalization
Mandy Lu, Qingyu Zhao, Jiequan Zhang +4
Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these tech…
Longitudinal Self-Supervised Learning
Qingyu Zhao, Zixuan Liu, Ehsan Adeli +1
Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative. As gr…
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…
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…
Variational AutoEncoder For Regression: Application to Brain Aging Analysis
Qingyu Zhao, Ehsan Adeli, Nicolas Honnorat +2
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close th…
Truncated Gaussian-Mixture Variational AutoEncoder
Qingyu Zhao, Nicolas Honnorat, Ehsan Adeli +1
Variation Autoencoder (VAE) has become a powerful tool in modeling the non-linear generative process of data from a low-dimensional latent space. Recently, several studies have pro…