13 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…
GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
Favour Nerrise, Lucy Yin, Mohammad H. Abbasi +2
Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Standard sparse autoencoders (SAE…
Modality-Aware and Anatomical Vector-Quantized Autoencoding for Multimodal Brain MRI
Mingjie Li, Edward Kim, Yue Zhao +2
Learning a robust Variational Autoencoder (VAE) is a fundamental step for many deep learning applications in medical image analysis, such as MRI synthesizes. Existing brain VAEs pr…
A Generative Foundation Model for Multimodal Histopathology
Jinxi Xiang, Mingjie Li, Siyu Hou +9
Accurate diagnosis and treatment of complex diseases require integrating histological, molecular, and clinical data, yet in practice these modalities are often incomplete owing to…
Diffusion MRI Transformer with a Diffusion Space Rotary Positional Embedding (D-RoPE)
Gustavo Chau Loo Kung, Mohammad Abbasi, Camila Blank +6
Diffusion Magnetic Resonance Imaging (dMRI) plays a critical role in studying microstructural changes in the brain. It is, therefore, widely used in clinical practice; yet progress…
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…