11 citations · 43 across the 28 of their papers we have counts for
29 papers
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…
WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs
Bahram Jafrasteh, Wei Peng, Cheng Wan +3
Generative models enhance neuroimaging through data augmentation, quality improvement, and rare condition studies. Despite advances in realistic synthetic MRIs, evaluations focus o…
Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning
Yixin Wang, Wei Peng, Yu Zhang +3
Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing…
Latent Causal Modeling for 3D Brain MRI Counterfactuals
Wei Peng, Tian Xia, Fabio De Sousa Ribeiro +5
The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectiv…
Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation
Jensen Hwa, Qingyu Zhao, Aditya Lahiri +3
Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Tra…
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…
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…
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding
Stephen Su, Samuel Kwong, Qingyu Zhao +3
There has been an increasing interest in multi-task learning for video understanding in recent years. In this work, we propose a generalized notion of multi-task learning by incorp…
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…
Longitudinal Correlation Analysis for Decoding Multi-Modal Brain Development
Qingyu Zhao, Ehsan Adeli, Kilian M. Pohl
Starting from childhood, the human brain restructures and rewires throughout life. Characterizing such complex brain development requires effective analysis of longitudinal and mul…
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…
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…
Representation Disentanglement for Multi-modal brain MR Analysis
Jiahong Ouyang, Ehsan Adeli, Kilian M. Pohl +2
Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested tha…
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Zixuan Liu, Ehsan Adeli, Kilian M. Pohl +1
Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision pr…
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…
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…