2 citations · 5 across the 10 of their papers we have counts for
10 papers
From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement
Xinyi Wang, Michael Barnett, Frederique Boonstra +15
Fiber orientation distribution (FOD) is an advanced diffusion MRI modeling technique that represents complex white matter fiber configurations, and a key step for subsequent brain…
Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging
Sheng Chen, Zihao Tang, Mariano Cabezas +6
Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules, offering the capability to inspect tissue…
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…
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
Dongang Wang, Peilin Liu, Hengrui Wang +11
Training deep neural networks reliably requires access to large-scale datasets. However, obtaining such datasets can be challenging, especially in the context of neuroimaging analy…
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…
Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation
Geng Zhan, Dongang Wang, Mariano Cabezas +5
Brain atrophy is an important biomarker for monitoring neurodegeneration and disease progression in conditions such as multiple sclerosis (MS). An accurate and robust quantitative…
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…
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement
Dongnan Liu, Chaoyi Zhang, Yang Song +4
Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-…