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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
收藏  |  浏览/下载：120/0  |  提交时间：2023/11/16
Magnetic particle imaging  Deblurring  Unpaired data  Contrastive learning  Adversarial framework  
Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk 期刊论文
TRANSLATIONAL LUNG CANCER RESEARCH, 2022, 页码: 23
作者:  He, Bing-Xi;  Zhong, Yi-Fan;  Zhu, Yong-Bei;  Deng, Jia-Jun;  Fang, Meng-Jie;  She, Yun-Lang;  Wang, Ting-Ting;  Yang, Yang;  Sun, Xi-Wen;  Belluomini, Lorenzo;  Watanabe, Satoshi;  Dong, Di;  Tian, Jie;  Xie, Dong
Adobe PDF(3742Kb)  |  收藏  |  浏览/下载：317/39  |  提交时间：2022/06/10
Tumor biomarkers  immunotherapy  lung neoplasms  programmed cell death 1 receptor (PD-1 receptor)  biostatistics  
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：670/358  |  提交时间：2021/05/06
deep learning  peritumor  radiomics  
Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images 期刊论文
Theranostics, 2020, 卷号: 0, 期号: 0, 页码: 0
作者:  Tian, Panwen;  He, Bingxi;  Dong, Di;  Mu, Wei;  Liu, Kunqin;  Liu, Li;  Zeng, Hao;  Liu, Yujie;  Jiang, Lili;  Zhou, Ping;  Huang, Zhipei;  Li, Weimin;  Tian, Jie
浏览  |  Adobe PDF(1539Kb)  |  收藏  |  浏览/下载：283/114  |  提交时间：2020/10/25
PD-L1 expression  deep learning  computed tomography  immunotherapy  non-small cell lung cancer.  
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study 期刊论文
ANNALS OF ONCOLOGY, 2020, 卷号: 31, 期号: 7, 页码: 912-920
作者:  Dong, Di;  Fang, Mengjie;  Tang, Lei;  Shan, Xiuhong;  Gao, Jianbo;  Giganti, Francesco;  Wang, Rongpin;  Chen, Xin;  Wang, Xiaoxiao;  Palumbo, Diego;  Fu, Jia;  Li, Wuchao;  Li, Jing;  Zhong, Lianzhen;  De Cobelli, Francesco;  Ji, Jiafu;  Liu, Zaiyi;  Tian, Jie
浏览  |  Adobe PDF(2209Kb)  |  收藏  |  浏览/下载：401/64  |  提交时间：2020/07/20
deep learning  locally advanced gastric cancer  lymph node metastasis  radiomic nomogram  
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：333/53  |  提交时间：2020/06/22
cervical cancer  MRI  radiomics  treatment response prediction  concurrent chemotherapy and radiation therapy  precision medicine  
Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer 期刊论文
RADIOTHERAPY AND ONCOLOGY, 2020, 卷号: 145, 期号: 1, 页码: 13-20
作者:  Zhang, Wenjuan;  Fang, Mengjie;  Dong, Di;  Wang, Xiaoxiao;  Ke, Xiaoai;  Zhang, Liwen;  Hu, Chaoen;  Guo, Lingyun;  Guan, Xiaoying;  Zhou, Junlin;  Shan, Xiuhong;  Tian, Jie
浏览  |  Adobe PDF(1203Kb)  |  收藏  |  浏览/下载：320/76  |  提交时间：2020/06/22
Gastric cancer  Computed tomography  Radiomics  Deep learning  Prognosis  
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：528/156  |  提交时间：2019/04/30