11 citations · 43 across the 28 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…