6 papers
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…
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…
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…
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…
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…
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…