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A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality 期刊论文
Computer Methods and Programs in Biomedicine, 2024, 卷号: 252, 页码: 108250-108250
作者:  Yusong Shen;  Liwen Zhang;  Hui Hui;  Lishuang Guo;  Tan Wang;  Guanyu Yang;  Jie Tian
Adobe PDF(6711Kb)  |  收藏  |  浏览/下载：57/21  |  提交时间：2024/06/11
Magnetic particle imaging  Simulation model  Parallel computing  Simulation software tool  
Diff-pcg: diffusion point cloud generation conditioned on continuous normalizing flow 期刊论文
The Visual Computer, 2024, 页码: 1-15
作者:  Yu T(余挺);  Meng WL(孟维亮);  Wu ZQ(吴仲琦);  Guo JW(郭建伟);  Zhang XP(张晓鹏)
Adobe PDF(2471Kb)  |  收藏  |  浏览/下载：45/13  |  提交时间：2024/06/11
3D shape generation  Diffusion model  Continuous normalizing flow  Point cloud  
DARTScore: DuAl-Reconstruction Transformer for Video Captioning Evaluation 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2024, 卷号: 34, 期号: 4, 页码: 2041-2055
作者:  Chen, Yuxin;  Zhang, Ziqi;  Qi, Zhongang;  Yuan, Chunfeng;  Wang, Jie;  Shan, Ying;  Li, Bing;  Hu, Weiming;  Qie, Xiaohu;  Wu, Jianping
Adobe PDF(13765Kb)  |  收藏  |  浏览/下载：58/5  |  提交时间：2024/05/30
Chinese video captioning evaluation  dual-reconstruction transformer  
Exploring Intrinsic Discrimination and Consistency for Weakly Supervised Object Localization 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2024, 卷号: 33, 期号: 0, 页码: 1045 - 1058
作者:  Changwei Wang;  Rongtao Xu;  Shibiao Xu;  Weiliang Meng;  Ruisheng Wang;  Xiaopeng Zhang
Adobe PDF(3269Kb)  |  收藏  |  浏览/下载：76/27  |  提交时间：2024/05/29
Weakly supervised object localization  intrinsic discrimination and consistency  deep metric learning  geometric transformation consistency  
A Streamlined 3-D Magnetic Particle Imaging System With a Two-Stage Excitation Feed-Through Compensation Strategy 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 卷号: 72, 页码: 1-10
作者:  Yin L(尹琳);  Li W(李玮);  Bian ZW(卞忠伟);  Chen ZW(陈梓威);  Liu YJ(刘晏君);  Zhong J(钟景);  Zhang SX(张水兴);  Du Y(杜洋);  Hui H(惠辉);  Tian J(田捷)
Adobe PDF(3893Kb)  |  收藏  |  浏览/下载：76/36  |  提交时间：2024/03/26
3-D imaging  compensation strategy  magnetic particle imaging (MPI)  
DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging 期刊论文
PHYSICS IN MEDICINE AND BIOLOGY, 2024, 卷号: 69, 期号: 1, 页码: 15
作者:  Peng, Zhengyao;  Yin, Lin;  Sun, Zewen;  Liang, Qian;  Ma, Xiaopeng;  An, Yu;  Tian, Jie;  Du, Yang
Adobe PDF(1035Kb)  |  收藏  |  浏览/下载：132/11  |  提交时间：2024/02/22
magnetic particle imaging  end-to-end reconstruction  deep learning  image reconstruction  
Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging 期刊论文
PHYSICS IN MEDICINE AND BIOLOGY, 2023, 卷号: 68, 期号: 14, 页码: 17
作者:  Zhang, Peng;  Liu, Jie;  Li, Yimeng;  Zhu, Tao;  Yin, Lin;  An, Yu;  Zhong, Jing;  Hui, Hui;  Tian, Jie
收藏  |  浏览/下载：224/0  |  提交时间：2023/11/17
magnetic particle imaging  reconstruction method  inverse problem  
Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework 期刊论文
COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 卷号: 165, 页码: 11
作者:  Zhang, Jiaxin;  Wei, Zechen;  Wu, Xiangjun;  Shang, Yaxin;  Tian, Jie;  Hui, Hui
Adobe PDF(6341Kb)  |  收藏  |  浏览/下载：208/20  |  提交时间：2023/11/16
Magnetic particle imaging  Deblurring  Unpaired data  Contrastive learning  Adversarial framework  
Excitation-based fully connected network for precise NIR-II fluorescence molecular tomography 期刊论文
BIOMEDICAL OPTICS EXPRESS, 2022, 卷号: 13, 期号: 12, 页码: 6284-6299
作者:  Cao, Caiguang;  Xiao, Anqi;  Cai, Meishan;  Shen, Biluo;  Guo, Lishuang;  Shi, Xiaojing;  Tian, Jie;  Hu, Zhenhua
Adobe PDF(6713Kb)  |  收藏  |  浏览/下载：496/61  |  提交时间：2023/03/20
Discrete soft actor-critic with auto-encoder on vascular robotic system 期刊论文
ROBOTICA, 2022, 页码: 12
作者:  Li, Hao;  Zhou, Xiao-Hu;  Xie, Xiao-Liang;  Liu, Shi-Qi;  Gui, Mei-Jiang;  Xiang, Tian-Yu;  Wang, Jin-Li;  Hou, Zeng-Guang
Adobe PDF(715Kb)  |  收藏  |  浏览/下载：287/9  |  提交时间：2023/01/09
surgical robots  vascular robotic system  automation  reinforcement learning  deep neural network