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Cross-Phase Adversarial Domain Adaptation for Deep Disease-free Survival Prediction with Gastric Cancer CT Images 会议论文
, Mexico, Oct 31 - Nov 4, 2021
作者:  Wang Siwen;  Dong Di;  Li Hailin;  Feng Caizhen;  Wang Yi;  Tian Jie
Adobe PDF(1071Kb)  |  收藏  |  浏览/下载：233/53  |  提交时间：2022/06/14
Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study 期刊论文
MEDICAL PHYSICS, 2021, 页码: 12
作者:  Wu, Xiangjun;  Dong, Di;  Zhang, Lu;  Fang, Mengjie;  Zhu, Yongbei;  He, Bingxi;  Ye, Zhaoxiang;  Zhang, Minming;  Zhang, Shuixing;  Tian, Jie
Adobe PDF(3862Kb)  |  收藏  |  浏览/下载：741/373  |  提交时间：2021/05/06
deep learning  peritumor  radiomics  
Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer 期刊论文
FRONTIERS IN ONCOLOGY, 2020, 卷号: 10, 页码: 8
作者:  Fang, Mengjie;  Kan, Yangyang;  Dong, Di;  Yu, Tao;  Zhao, Nannan;  Jiang, Wenyan;  Zhong, Lianzhen;  Hu, Chaoen;  Luo, Yahong;  Tian, Jie
Adobe PDF(730Kb)  |  收藏  |  浏览/下载：383/65  |  提交时间：2020/06/22
cervical cancer  MRI  radiomics  treatment response prediction  concurrent chemotherapy and radiation therapy  precision medicine  
Diagnostic accuracy of dual-energy CT-based nomograms to predict lymph node metastasis in gastric cancer 期刊论文
EUROPEAN RADIOLOGY, 2018, 卷号: 28, 期号: 12, 页码: 5241-5249
作者:  Li, Jing;  Fang, Mengjie;  Wang, Rui;  Dong, Di;  Tian, Jie;  Liang, Pan;  Liu, Jie;  Gao, Jianbo
Adobe PDF(2248Kb)  |  收藏  |  浏览/下载：347/40  |  提交时间：2019/07/12
Gastric cancer  Dual-energy CT  Lymph node metastasis  Nomogram  
Unsupervised Deep Learning Features for Lung Cancer Overall Survival Analysis 会议论文
, Honolulu, Hawaii, USA, 2018-7
作者:  Wang, Shuo;  Liu, Zhenyu;  Chen, Xi;  Zhu, Yongbei;  Zhou, Hongyu;  Tang, Zhenchao;  Wei, Wei;  Dong, Di;  Wang, Meiyun;  Tian, Jie
Adobe PDF(797Kb)  |  收藏  |  浏览/下载：452/136  |  提交时间：2019/04/30
Lung Cancer  Survival Analysis  Deep Learning  Unsupervised Feature Learning  Convolutional Neural Networks  
A Multi-view Deep Convolutional Neural Networks for Lung Nodule Segmentation 会议论文
, Jeju Island, Korea, 2017-7
作者:  Wang, Shuo;  Zhou, Mu;  Gevaert, Olivier;  Tang, Zhenchao;  Dong, Di;  Liu, Zhenyu;  Tian, Jie
浏览  |  Adobe PDF(962Kb)  |  收藏  |  浏览/下载：408/162  |  提交时间：2019/04/30
Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning 期刊论文
EUROPEAN RESPIRATORY JOURNAL, 2019, 卷号: 53, 期号: 3, 页码: 11
作者:  Wang, Shuo;  Shi, Jingyun;  Ye, Zhaoxiang;  Dong, Di;  Yu, Dongdong;  Zhou, Mu;  Liu, Ying;  Gevaert, Olivier;  Wang, Kun;  Zhu, Yongbei;  Zhou, Hongyu;  Liu, Zhenyu;  Tian, Jie
浏览  |  Adobe PDF(743Kb)  |  收藏  |  浏览/下载：569/165  |  提交时间：2019/04/30
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：588/229  |  提交时间：2018/01/08
Lung Nodule Segmentation  Convolutional Neural Networks  Deep Learning  Computer-aided Diagnosis  
CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma 期刊论文
EUROPEAN JOURNAL OF RADIOLOGY, 2017, 卷号: 91, 期号: 2017, 页码: 142-147
作者:  Ma, Zelan;  Fang, Mengjie;  Huang, Yanqi;  He, Lan;  Chen, Xin;  Liang, Cuishan;  Huang, Xiaomei;  Cheng, Zixuan;  Dong, Di;  Liang, Changhong;  Xie, Jiajun;  Tian, Jie;  Liu, Zaiyi
浏览  |  Adobe PDF(515Kb)  |  收藏  |  浏览/下载：631/242  |  提交时间：2017/09/12
Borrmann Type Iv Gastric Cancer  Primary Gastric Lymphoma  Computed Tomography  Radiomics Signature  SubjeCtive Ct Findings  
In vivo pentamodal tomographic imaging for small animals 期刊论文
BIOMEDICAL OPTICS EXPRESS, 2017, 卷号: 8, 期号: 3, 页码: 1356-1371
作者:  Liu, Muhan;  Guo, Hongbo;  Liu, Hongbo;  Zhang, Zeyu;  Chi, Chongwei;  Hui, Hui;  Dong, Di;  Hu, Zhenhua;  Tian, Jie
浏览  |  Adobe PDF(8526Kb)  |  收藏  |  浏览/下载：376/102  |  提交时间：2017/05/05
Pentamodal Tomographic Imaging