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SWISTA-Nets: Subband-adaptive wavelet iterative shrinkage thresholding networks for image reconstruction 期刊论文
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2024, 卷号: 113, 页码: 12
作者:  Lu, Binchun;  Fu, Lidan;  Pan, Yixuan;  Dong, Yonggui
收藏  |  浏览/下载：8/0  |  提交时间：2024/05/30
Image reconstruction  Inverse problem  Deep learning  Iterative shrinkage-thresholding algorithm  Electromagnetic tomography  Sparse-view CT  
Dual Contrastive Learning with Adversarial Framework for Magnetic Particle Imaging Deblurring 会议论文
, Aachen, Germany, 2023-3-22
作者:  Zhang, Jiaxin;  Wei, Zechen;  Liu, Yuanduo;  Wu, Xiangjun;  Tian, Jie;  Hui, Hui
Adobe PDF(1341Kb)  |  收藏  |  浏览/下载：12/5  |  提交时间：2024/05/29
Fusion Networks of CNN and Transformer with Channel Attention for Accurate Tumor Imaging in Magnetic Particle Imaging 期刊论文
BIOLOGY-BASEL, 2024, 卷号: 13, 期号: 1, 页码: 17
作者:  Shang, Yaxin;  Liu, Jie;  Wang, Yueqi;  Bertrand, Helene
Adobe PDF(15462Kb)  |  收藏  |  浏览/下载：37/1  |  提交时间：2024/03/26
magnetic particle imaging  convolutional neural network  transformer  tumor imaging  accurate quantification  
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：86/3  |  提交时间：2024/02/22
magnetic particle imaging  end-to-end reconstruction  deep learning  image reconstruction  
Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy 期刊论文
BMC Medicine, 2022, 卷号: 20, 期号: 1, 页码: 1-13
作者:  Zhu,Yangyang;  Meng,Zheling;  Fan,Xiao;  Duan,Yin;  Jia,Yingying;  Dong,Tiantian;  Wang,Yanfang;  Song,Juan;  Tian,Jie;  Wang,Kun;  Nie,Fang
Adobe PDF(3297Kb)  |  收藏  |  浏览/下载：256/38  |  提交时间：2022/09/19
Deep learning  Cervical lymphadenopathy  Ultrasound  Reactive hyperplasia  Tuberculous lymphadenitis  Lymphoma  Metastatic carcinoma  
A novel matrix used in regularization term for model-based photoacoustic reconstructions 会议论文
, San Francisco, CA, United states, January 28, 2018 - February 1, 2018
作者:  Tong, Tong;  Wang, Kun;  Tian, Jie
Adobe PDF(757Kb)  |  收藏  |  浏览/下载：125/46  |  提交时间：2022/06/14
Photoacoustic tomography  Image reconstruction  Regularization  Central-Enhancement Laplace matrix  
Predicting histopathological findings of gastric cancer via deep generalized multi-instance learning 会议论文
, San Diego, California, USA, 2019-2
作者:  Fang, Mengjie;  Zhang, Wenjuan;  Dong, Di;  Zhou, Junlin;  Tian, Jie
Adobe PDF(500Kb)  |  收藏  |  浏览/下载：158/61  |  提交时间：2022/06/14
A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space 期刊论文
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2022, 卷号: 41, 期号: 3, 页码: 657-666
作者:  An, Yu;  Bian, Chang;  Yan, Daxiang;  Wang, Hanfan;  Wang, Yu;  Du, Yang;  Tian, Jie
Adobe PDF(8285Kb)  |  收藏  |  浏览/下载：324/62  |  提交时间：2022/06/06
Imaging  Image reconstruction  Mice  In vivo  Image segmentation  Finite element analysis  Surface reconstruction  Fluorescence molecular tomography  imaging reconstruction  standardized imaging space  
A review of the application of machine learning in molecular imaging 期刊论文
Annals of Translational Medicine, 2021, 卷号: 0, 期号: 0, 页码: 0
作者:  Yin, Lin;  Cao, Zhen;  Wang, Kun;  Tian, Jie;  Yang, Xing;  Zhang, Jianhua
Adobe PDF(4435Kb)  |  收藏  |  浏览/下载：194/38  |  提交时间：2021/06/16
molecular imaging, machine learning, artificial intelligence  
Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study 期刊论文
CANCERS, 2021, 卷号: 13, 期号: 10, 页码: 19
作者:  Wei, Jingwei;  Jiang, Hanyu;  Zeng, Mengsu;  Wang, Meiyun;  Niu, Meng;  Gu, Dongsheng;  Chong, Huanhuan;  Zhang, Yanyan;  Fu, Fangfang;  Zhou, Mu;  Chen, Jie;  Lyv, Fudong;  Wei, Hong;  Bashir, Mustafa R.;  Song, Bin;  Li, Hongjun;  Tian, Jie
Adobe PDF(2568Kb)  |  收藏  |  浏览/下载：374/61  |  提交时间：2021/06/15
hepatocellular carcinoma  microvascular invasion  magnetic resonance imaging  computed tomography  deep learning