3 citations · 6 across the 4 of their papers we have counts for
8 papers
Federated Learning for Large Models in Medical Imaging: A Comprehensive Review
Mengyu Sun, Ziyuan Yang, Yongqiang Huang +5
Artificial intelligence (AI) has demonstrated considerable potential in the realm of medical imaging. However, the development of high-performance AI models typically necessitates…
FedRIR: Rethinking Information Representation in Federated Learning
Yongqiang Huang, Zerui Shao, Ziyuan Yang +2
Mobile and Web-of-Things (WoT) devices at the network edge generate vast amounts of data for machine learning applications, yet privacy concerns hinder centralized model training.…
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Zhiwen Wang, Wenjun Xia, Zexin Lu +5
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow. It is easy to notic…
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
Tao Wang, Wenjun Xia, Yongqiang Huang +5
Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and…
CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels
Wenjun Xia, Zexin Lu, Yongqiang Huang +4
Current mainstream of CT reconstruction methods based on deep learning usually needs to fix the scanning geometry and dose level, which will significantly aggravate the training co…
MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction
Wenjun Xia, Zexin Lu, Yongqiang Huang +6
Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstru…