1 citations · 4 across the 10 of their papers we have counts for
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Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images
Hualiang Wang, Yiqun Lin, Xinpeng Ding +1
General networks for 3D medical image segmentation have recently undergone extensive exploration. Behind the exceptional performance of these networks lies a significant demand for…
Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction
Yiqun Lin, Hualiang Wang, Jixiang Chen +1
Cone-Beam Computed Tomography (CBCT) is an indispensable technique in medical imaging, yet the associated radiation exposure raises concerns in clinical practice. To mitigate these…
Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation
Marawan Elbatel, Hualiang Wang, Jixiang Chen +2
Federated semi-supervised learning (FedSemi) refers to scenarios where there may be clients with fully labeled data, clients with partially labeled, and even fully unlabeled client…
C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
Yiqun Lin, Jiewen Yang, Hualiang Wang +3
Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios, such as diagnosis and preoperative planning. Using fewer projection views…