63 citations · 119 across the 41 of their papers we have counts for
6 papers · 1 filter
Integrating Anatomical Priors into a Causal Diffusion Model
Binxu Li, Wei Peng, Mingjie Li +2
3D brain MRI studies often examine subtle morphometric differences between cohorts that are hard to detect visually. Given the high cost of MRI acquisition, these studies could gre…
Evaluation of 3D Counterfactual Brain MRI Generation
Pengwei Sun, Wei Peng, Lun Yu Li +2
Counterfactual generation offers a principled framework for simulating hypothetical changes in medical imaging, with potential applications in understanding disease mechanisms and…
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…
Generating Novel Brain Morphology by Deforming Learned Templates
Alan Q. Wang, Fangrui Huang, Bailey Trang +5
Designing generative models for 3D structural brain MRI that synthesize morphologically-plausible and attribute-specific (e.g., age, sex, disease state) samples is an active area o…
Diffusion Models for Computational Neuroimaging: A Survey
Haokai Zhao, Haowei Lou, Lina Yao +4
Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and behavior. While diffusion models…