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Deep Pyramid Local Attention Neural Network for Cardiac Structure Segmentation in Two-dimensional Echocardiography 期刊论文
Medical Image Analysis, 2021, 卷号: 67, 期号: 67, 页码: 101873
作者:  Fei Liu;  Wang K(王坤);  Dan Liu;  Xin Yang;  Jie Tian
浏览  |  Adobe PDF(3848Kb)  |  收藏  |  浏览/下载:410/110  |  提交时间:2020/11/02
2D echocardiography  Cardiac structure segmentation  Pyramid local attention  Label coherence learning  
CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer 期刊论文
EUROPEAN JOURNAL OF RADIOLOGY, 2020, 卷号: 132, 页码: 8
作者:  Sun, Rui-Jia;  Fang, Meng-Jie;  Tang, Lei;  Li, Xiao-Ting;  Lu, Qiao-Yuan;  Dong, Di;  Tian, Jie;  Sun, Ying-Shi
Adobe PDF(3491Kb)  |  收藏  |  浏览/下载:280/50  |  提交时间:2021/01/06
Stomach neoplasms  Multi-detector computed tomography  Radiomics  Deep learning  
Radiomics in liver diseases: Current progress and future opportunities 期刊论文
LIVER INTERNATIONAL, 2020, 卷号: 40, 期号: 9, 页码: 2050-2063
作者:  Wei, Jingwei;  Jiang, Hanyu;  Gu, Dongsheng;  Niu, Meng;  Fu, Fangfang;  Han, Yuqi;  Song, Bin;  Tian, Jie
Adobe PDF(872Kb)  |  收藏  |  浏览/下载:434/125  |  提交时间:2020/08/03
data science  liver diseases  machine learning  precision medicine  radiologic technology  
Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences 期刊论文
Medical Image Analysis, 2020, 卷号: 65, 期号: 65, 页码: 101793
作者:  Fei Liu;  Dan Liu;  Jie Tian;  Xiaoyan Xie;  Xin Yang;  Wang K(王坤)
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Ultrasound sequence  Respiratory motion estimation  Cascaded Siamese network  One-shot deformable convolution  
Application of machine learning method in optical molecular imaging: a review 期刊论文
SCIENCE CHINA-INFORMATION SCIENCES, 2020, 卷号: 63, 期号: 1, 页码: 16
作者:  An, Yu;  Meng, Hui;  Gao, Yuan;  Tong, Tong;  Zhang, Chong;  Wang, Kun;  Tian, Jie
Adobe PDF(5942Kb)  |  收藏  |  浏览/下载:329/56  |  提交时间:2020/03/30
optical molecular imaging  machine learning  artificial intelligence  
Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images 期刊论文
BIOMEDICAL OPTICS EXPRESS, 2019, 卷号: 10, 期号: 9, 页码: 4742-4756
作者:  Zhang, Chong;  Wang, Kun;  An, Yu;  He, Kunshan;  Tong, Tong;  Tian, Jie
浏览  |  Adobe PDF(4570Kb)  |  收藏  |  浏览/下载:269/77  |  提交时间:2019/09/26
Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas 期刊论文
JOURNAL OF NEURO-ONCOLOGY, 2018, 卷号: 140, 期号: 2, 页码: 297-306
作者:  Han, Yuqi;  Xie, Zhen;  Zang, Yali;  Zhang, Shuaitong;  Gu, Dongsheng;  Zhou, Mu;  Gevaert, Olivier;  Wei, Jingwei;  Li, Chao;  Chen, Hongyan;  Du, Jiang;  Liu, Zhenyu;  Dong, Di;  Tian, Jie;  Zhou, Dabiao
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Lower-grade glioma  1p19q Co-deletion  Prediction  Radiomics  Magnetic resonance imaging  
Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer 期刊论文
TRANSLATIONAL ONCOLOGY, 2018, 卷号: 11, 期号: 1, 页码: 94-101
作者:  Zhang, Liwen;  Chen, Bojiang;  Liu, Xia;  Song, Jiangdian;  Fang, Mengjie;  Hu, Chaoen;  Dong, Di;  Li, Weimin;  Tian, Jie
Adobe PDF(1030Kb)  |  收藏  |  浏览/下载:428/121  |  提交时间:2018/01/09
Radiomics  
Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer 期刊论文
CLINICAL CANCER RESEARCH, 2017, 卷号: 23, 期号: 23, 页码: 7253-7262
作者:  Liu, Zhenyu;  Zhang, Xiao-Yan;  Shi, Yan-Jie;  Wang, Lin;  Zhu, Hai-Tao;  Tang, Zhenchao;  Wang, Shuo;  Li, Xiao-Ting;  Tian, Jie;  Sun, Ying-Shi
浏览  |  Adobe PDF(939Kb)  |  收藏  |  浏览/下载:911/430  |  提交时间:2018/01/13
Radiomics  
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation 期刊论文
MEDICAL IMAGE ANALYSIS, 2017, 卷号: 40, 期号: 40, 页码: 172-183
作者:  Wang, Shuo;  Zhou, Mu;  Liu, Zaiyi;  Liu, Zhenyu;  Gu, Dongsheng;  Zang, Yali;  Dong, Di;  Gevaert, Olivier;  Tian, Jie
浏览  |  Adobe PDF(3023Kb)  |  收藏  |  浏览/下载:548/222  |  提交时间:2018/01/08
Lung Nodule Segmentation  Convolutional Neural Networks  Deep Learning  Computer-aided Diagnosis