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Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid Nodules With Ultrasound Images 期刊论文
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2020, 卷号: 67, 期号: 10, 页码: 2773-2780
作者:  Zhou, Hui;  Wang, Kun;  Tian, Jie
收藏  |  浏览/下载：195/0  |  提交时间：2021/01/07
Cancer  Biological system modeling  Ultrasonic imaging  Feature extraction  Radiomics  Training  Deep learning  Diagnosis  online learning  radiomics  transfer learning  thyroid nodules  ultrasound images  
Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography 期刊论文
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2020, 卷号: 67, 期号: 7, 页码: 2023-2032
作者:  Yin, Lin;  Wang, Kun;  Tong, Tong;  An, Yu;  Meng, Hui;  Yang, Xin;  Tian, Jie
浏览  |  Adobe PDF(646Kb)  |  收藏  |  浏览/下载：308/55  |  提交时间：2020/08/03
Image reconstruction  Bayes methods  Tumors  Inverse problems  Light sources  Tomography  Morphology  Bioluminescence tomography (BLT)  block sparse Bayesian learning  morphology recovery  
Fluorescence Molecular Tomography Based on Group Sparsity Priori for Morphological Reconstruction of Glioma 期刊论文
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2020, 卷号: 67, 期号: 5, 页码: 1429-1437
作者:  Jiang, Shixin;  Liu, Jie;  An, Yu;  Gao, Yuan;  Meng, Hui;  Wang, Kun;  Tian, Jie
收藏  |  浏览/下载：215/0  |  提交时间：2020/06/22
Fluorescence  Image reconstruction  Tumors  Reconstruction algorithms  Morphology  Photonics  Inverse problems  Fluorescence molecular tomography  group sparsity  image reconstruction  morphological reconstruction  
Reconstruction of Fluorescence Molecular Tomography via a Fused LASSO Method Based on Group Sparsity Prior 期刊论文
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2019, 卷号: 66, 期号: 5, 页码: 1361-1371
作者:  Jiang, Shixin;  Liu, Jie;  Zhang, Guanglei;  An, Yu;  Meng, Hui;  Gao, Yuan;  Wang, Kun;  Tian, Jie
浏览  |  Adobe PDF(5641Kb)  |  收藏  |  浏览/下载：482/120  |  提交时间：2019/07/11
Fluorescence molecular tomography  image reconstruction  fused LASSO method  group sparsity