10 papers
Pixel-Space Diffusion Transformers
Renye Yan, Jikang Cheng, You Wu +9
Latent diffusion models (LDMs) enable efficient high-resolution image synthesis by denoising in a VAE-compressed latent space. However, fixed visual tokenizers can discard fine tex…
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…
Cycle Diffusion Model for Counterfactual Image Generation
Fangrui Huang, Alan Wang, Binxu Li +5
Deep generative models have demonstrated remarkable success in medical image synthesis. However, ensuring conditioning faithfulness and high-quality synthetic images for direct or…
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…
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…