1.9k citations · 5.3k across the 34 of their papers we have counts for
35 papers
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging
Ziyuan Yang, Wenjun Xia, Zexin Lu +3
Computed tomography (CT) is of great importance in clinical practice due to its powerful ability to provide patients' anatomical information without any invasive inspection, but it…
NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery
Ming Lu, Leyuan Fang, Muxing Li +3
The use of deep learning for water extraction requires precise pixel-level labels. However, it is very difficult to label high-resolution remote sensing images at the pixel level.…
LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes
Jiahui Ni, Zhimin Shao, Zhongzhou Zhang +4
Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchroma…
Unsupervised PET Reconstruction from a Bayesian Perspective
Chenyu Shen, Wenjun Xia, Hongwei Ye +5
Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve i…
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…
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Tao Wang, Wenjun Xia, Zexin Lu +5
Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several network mo…
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information
Linchao He, Mengting Luo, Dejun Zhang +3
Point cloud analysis is still a challenging task due to the disorder and sparsity of samplings of their geometric structures from 3D sensors. In this paper, we introduce the homoto…
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…
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Yi Zhang, Hu Chen, Wenjun Xia +5
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis a…
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography
Xiang Chen, Wenjun Xia, Yan Liu +3
Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs). However, due to the limited photons and countin…
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…
Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
Zhuonan He, Yikun Zhang, Yu Guan +4
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction is one of the most promising ways to compen…
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…
Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images
Yongqiang Huang, Wenjun Xia, Zexin Lu +5
Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging…
Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection
Zerui Shao, Yifei Pu, Jiliu Zhou +2
Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Compon…
MD-Recon-Net: A Parallel Dual-Domain Convolutional Neural Network for Compressed Sensing MRI
Maosong Ran, Wenjun Xia, Yongqiang Huang +6
Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that can accurately reconstruct images from undersampled k-space data with a much lower sampling r…
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
Peng Bao, Wenjun Xia, Kang Yang +9
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, traditional DL-based computed tomogra…
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks
Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang +2
In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coh…
Sparse-View CT Reconstruction via Convolutional Sparse Coding
Peng Bao, Wenjun Xia, Kang Yang +2
Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To…
Visual Attention Network for Low Dose CT
Wenchao Du, Hu Chen, Peixi Liao +3
Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance. Perfect noise removal and image restoration is intr…
Asymptotic dynamic for dipolar Quantum Gases below the ground state energy threshold
Jacopo Bellazzini, Luigi Forcella
We consider the Gross-Pitaevskii equation describing a dipolar Bose-Einstein condensate without external confinement. We first consider the unstable regime, where the nonlocal nonl…
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
Chenyu You, Qingsong Yang, Hongming Shan +8
Computed tomography (CT) is a popular medical imaging modality in clinical applications. At the same time, the x-ray radiation dose associated with CT scans raises public concerns…
Few-View CT Reconstruction with Group-Sparsity Regularization
Peng Bao, Jiliu Zhou, Yi Zhang
Classical total variation (TV) based iterative reconstruction algorithms assume that the signal is piecewise smooth, which causes reconstruction results to suffer from the over-smo…
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network
Hongming Shan, Yi Zhang, Qingsong Yang +5
Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction…