2 citations · 5 across the 10 of their papers we have counts for
5 papers · 1 filter
Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis
Yang Ma, Dongang Wang, Peilin Liu +4
The heterogeneity of neurological conditions, ranging from structural anomalies to functional impairments, presents a significant challenge in medical imaging analysis tasks. Moreo…
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Lei Bai, Dongang Wang, Michael Barnett +13
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct ther…
Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI
Sheng Chen, Zihao Tang, Dongnan Liu +5
Precise thigh muscle volumes are crucial to monitor the motor functionality of patients with diseases that may result in various degrees of thigh muscle loss. T1-weighted MRI is th…
TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging
Zihao Tang, Xinyi Wang, Lihaowen Zhu +5
Diffusion Weighted Imaging (DWI) is an advanced imaging technique commonly used in neuroscience and neurological clinical research through a Diffusion Tensor Imaging (DTI) model. V…
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning
Dongnan Liu, Mariano Cabezas, Dongang Wang +16
Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's…