1 citations · 2 across the 5 of their papers we have counts for
6 papers
Beyond Feature Reliability: Repeat-Informed Multifractal Curve Regression for Brain-Age Prediction
Yu Chang, Anzhe Cheng, Jiahao Chen +8
Brain-age prediction from resting-state fMRI provides a quantitative framework for characterizing age-related changes in spontaneous brain dynamics and for identifying functional s…
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
Yu Chang, Anzhe Cheng, Chenwei Wu +7
The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction. Recent multimodal models have adv…
Multi-modal Imputation for Alzheimer's Disease Classification
Abhijith Shaji, Tamoghna Chattopadhyay, Sophia I. Thomopoulos +3
Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modaliti…
ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization
Anzhe Cheng, Shukai Duan, Shixuan Li +8
Mixture-of-Experts (MoE) architectures expand model capacity by sparsely activating experts but face two core challenges: misalignment between router logits and each expert's inter…
Diffusion Bridge Models for 3D Medical Image Translation
Shaorong Zhang, Tamoghna Chattopadhyay, Sophia I. Thomopoulos +3
Diffusion tensor imaging (DTI) provides crucial insights into the microstructure of the human brain, but it can be time-consuming to acquire compared to more readily available T1-w…
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models
Umang Gupta, Tamoghna Chattopadhyay, Nikhil Dhinagar +3
Transfer learning has remarkably improved computer vision. These advances also promise improvements in neuroimaging, where training set sizes are often small. However, various diff…