2 citations · 2 across the 6 of their papers we have counts for
6 papers
Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data
Michalis Pistos, Gang Li, Weili Lin +2
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity developme…
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint
Alaa Bessadok, Ahmed Nebli, Mohamed Ali Mahjoub +4
Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analys…
Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces
Peirong Liu, Zhengwang Wu, Gang Li +2
Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which…
Deep Fiducial Inference
Gang Li, Jan Hannig
Since the mid-2000s, there has been a resurrection of interest in modern modifications of fiducial inference. To date, the main computational tool to extract a generalized fiducial…
Spherical U-Net on Cortical Surfaces: Methods and Applications
Fenqiang Zhao, Shunren Xia, Zhengwang Wu +6
Convolutional Neural Networks (CNNs) have been providing the state-of-the-art performance for learning-related problems involving 2D/3D images in Euclidean space. However, unlike i…
Deep Morphological Simplification Network (MS-Net) for Guided Registration of Brain Magnetic Resonance Images
Dongming Wei, Zhengwang Wu, Gang Li +3
Objective: Deformable brain MR image registration is challenging due to large inter-subject anatomical variation. For example, the highly complex cortical folding pattern makes it…