1 citations · 1 across the 12 of their papers we have counts for
14 papers
Pixel-Space Diffusion Transformers
Renye Yan, Jikang Cheng, You Wu +8
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…
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…
Neural Autoregressive Modeling of Brain Aging
Ridvan Yesiloglu, Wei Peng, Md Tauhidul Islam +1
Brain aging synthesis is a critical task with broad applications in clinical and computational neuroscience. The ability to predict the future structural evolution of a subject's b…
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…
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…
Vision Language Models in Medicine
Beria Chingnabe Kalpelbe, Angel Gabriel Adaambiik, Wei Peng
With the advent of Vision-Language Models (VLMs), medical artificial intelligence (AI) has experienced significant technological progress and paradigm shifts. This survey provides…
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…
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…
Evaluating the Quality of Brain MRI Generators
Jiaqi Wu, Wei Peng, Binxu Li +2
Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part…
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…
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…