招收博士研究生、硕士研究生

081104-模式识别与智能系统
081203-计算机应用技术
1001Z1-精准医学

医学影像处理与分析
影像大数据智能分析
医疗大数据智能分析

2008-09--2013-06   中国科学院大学   博士研究生/工学博士学位
2004-09--2008-06   北京科技大学   本科/工学学士学位

博士研究生

工学博士学位

   

2020-10~现在, 中国科学院自动化研究所, 研究员
2015-11~2020-10,中国科学院自动化研究所, 副研究员
2013-07~2015-10,中国科学院自动化研究所, 助理研究员

2022-05-14-今,中国图学学会, 高级会员
2021-12-16-今,中国科学院青年创新促进会, 优秀会员
2020-10-24-今,北京癌症防治学会胃癌防治专业委员会, 常务委员

   

（1） 四川省科学技术进步一等奖“肺癌早期诊断的基础与关键技术研究”, 一等奖, 省级, 2019
（2） 2015年全国发明博览会金奖“光学分子影像微创手术导航系统”, 一等奖, 国家级, 2015
（3） 2014年国际发明展览会金奖“光学分子影像肿瘤靶向手术导航系统”, 一等奖, 国家级, 2014

[1] 田捷, 董迪, 李聪, 胡振华, 杨鑫, 胡朝恩. 基于Unet迁移学习的胃印戒细胞癌图像智能分类系统. CN: CN112861994A, 2021-05-28.

[2] 田捷, 惠辉, 胡朝恩, 董迪, 杨鑫. 基于乳腺癌检测的平板PET与光学双模融合成像系统及方法. CN: CN109589128B, 2020-12-11.

[3] 田捷, 董迪, 张利文, 杨鑫. 基于肿瘤影像的多区域的影像组学特征提取方法. CN: CN110427954A, 2019-11-08.

[4] 田捷, 孟慧, 惠辉, 董迪, 杨鑫. 基于CUDA加速的神经元活动图像动态配准方法及装置. CN: CN106384350B, 2019-06-07.

[5] 田捷, 胡朝恩, 惠辉, 董迪, 杨鑫. 一种基于多角度的选择性光片照明显微成像的重建方法. CN: CN106447717B, 2019-05-03.

[6] 田捷, 刘振宇, 沈忱, 董迪, 臧亚丽, 杨彩云, 徐敏. 一种基于影像组学的多模态磁共振图像差异检测方法及装置. CN: CN105931221B, 2019-03-15.

[7] 田捷, 梁潇, 董迪, 惠辉, 杨鑫, 徐敏. 基于梯度调整的血管图像增强方法. 中国: CN105654439B, 2018.09.11.

[8] 田捷, 梁潇, 董迪, 惠辉, 杨鑫, 徐敏. 基于先验模型的光片显微成像条纹噪声去除方法. 中国: CN104835123B, 2018-07-31.

[9] 田捷, 方梦捷, 董迪, 惠辉, 杨鑫, 徐敏. 光学投影断层成像的检测方法. 中国: CN104865195B, 2018-02-02.

[10] 田捷, 宋江典, 董迪, 臧亚丽, 刘振宇. 一种基于影像组学的病变组织辅助预后系统和方法. 中国: CN105653858A, 2016-06-08.

[11] 田捷, 杨玉洁, 董迪, 惠辉, 马喜波, 施亮亮, 汪俊. 基于仿体的光学投影断层成像系统的几何校正方法. 中国: CN104713864A, 2015.06.17.

[12] 田捷, 宋江典, 董迪, 张帅通, 喻冬东. 一种基于肿瘤表型特征的非小细胞肺癌预后方法. 中国: CN105005714A, 2015-10-28.

[13] 田捷, 张帅通, 董迪, 惠辉, 杨鑫, 徐敏. 一种基于仿体的混合成像三维配准方法. 中国: CN104992450A, 2015-10-21.

[14] 田捷, 张帅通, 董迪, 惠辉, 杨鑫, 徐敏. 图像配准方法. 中国: CN104820989A, 2015-08-05.

[15] 杨鑫, 惠辉, 田捷, 董迪, 马喜波. 多光子激发多向照明显微成像系统. 中国: CN104677872A, 2015-06-03.

[16] 杨鑫, 惠辉, 田捷, 董迪, 马喜波. 多光子激发光片照明显微成像系统. 中国: CN104677871A, 2015-06-03.

[17] 田捷, 惠辉, 董迪, 詹诗杰, 杨鑫. 一种结构-光学-核素多模态成像系统与方法. 中国: CN103800076A, 2014.05.21.

[18] 田捷, 惠辉, 董迪, 詹诗杰, 杨鑫. 一种光学多模态成像系统. 中国: CN103735251A, 2014-04-23.

[19] 田捷, 惠辉, 董迪, 詹诗杰, 杨鑫. 一种光学多模态成像系统与方法. 中国: CN103735252A, 2014-04-23.

[20] 田捷, 刘振宇, 董迪, 杨鑫, 王坤, 彭冬. 一种光声与X射线断层成像融合的成像系统. 中国: CN103690244A, 2014-04-02.

[21] 田捷, 詹诗杰, 惠辉, 董迪, 杨鑫. 一种监测仪器的干扰屏蔽伸缩式装置. 中国: CN203502578U, 2014-03-26.

[22] 田捷, 詹诗杰, 惠辉, 董迪, 杨鑫. 一种监测仪器的干扰屏蔽旋转装置. 中国: CN203490352U, 2014-03-19.

[23] 杨鑫, 刘振宇, 田捷, 董迪, 马喜波, 彭冬. 一种集成光声与X射线断层成像的分离式成像系统. 中国: CN103519789A, 2014-01-22.

[24] 田捷, 郭进, 董迪, 杨鑫, 马喜波, 杨玉洁. 一种基于景深融合的光学投影断层成像图像获取方法. 中国: CN103308452A, 2013.09.18.

[25] 田捷, 施亮亮, 董迪, 马喜波, 詹诗杰, 刘振宇. 一种分离式多模融合三维断层成像系统及其方法. 中国: CN103431912A, 2013-12-11.

[26] 田捷, 彭冬, 董迪, 杜洋, 杨鑫, 刘振宇. 一种光声和荧光循环互提升成像方法. 中国: CN103393408A, 2013-11-20.

[27] 田捷, 彭冬, 董迪, 杜洋, 杨鑫, 刘振宇. 一种光声和光学融合的多模态成像系统. 中国: CN103389273A, 2013-11-13.

[28] 田捷, 郭进, 董迪, 杨鑫, 马喜波, 施亮亮. 一种位置可调的光学投影断层成像系统样本固定装置. 中国: CN103364254A, 2013-10-23.

[29] 田捷, 刘振宇, 彭冬, 马喜波, 董迪, 徐敏. 一种基于迭代自适应加权的有限视角光声成像重建方法. 中国: CN103345770A, 2013-10-09.

[30] 田捷, 尤优博, 董迪, 杨鑫, 刘振宇, 卫文娟. 一种基于模态融合的大脑网络功能连接偏侧性检测方法. 中国: CN103345749A, 2013-10-09.

[31] 田捷, 杨玉洁, 董迪, 杨鑫, 詹诗杰, 郭进. 自动防辐射的FMT和CT双模成像系统. 中国: CN103330549A, 2013-10-02.

[32] 田捷, 尤优博, 杨鑫, 董迪, 刘振宇, 卫文娟. 一种基于模态融合的默认态脑网络中心节点检测方法. 中国: CN103325119A, 2013-09-25.

[33] 杨鑫, 尤优博, 田捷, 董迪, 杨玉洁, 郭进. 一种分离式多模态融合的三维成像系统. 中国: CN103300828A, 2013-09-18.

[34] 田捷, 董迪, 秦承虎, 杨鑫. 一种定位光学投影断层成像旋转中心的方法. 中国: CN102512140B, 2013-07-24.

[35] 田捷, 董迪, 杨鑫, 郭进. 基于图像重叠的选择性平面照射显微成像伪影去除方法. 中国: CN102982521A, 2013-03-20.

[36] 田捷, 郭进, 董迪, 杨鑫. 自动处理景深的光学投影断层成像方法. 中国: CN102973246A, 2013-03-20.

[37] 田捷, 董迪, 秦承虎, 杨鑫, 郭进, 马喜波. 光学投影断层成像系统. 中国: CN102743159A, 2012-10-24.

[38] 田捷, 董迪, 秦承虎, 杨鑫, 郭进, 马喜波. 一种基于拼合螺旋扫描方式的光学投影断层成像方法. 中国: CN102727188A, 2012-10-17.

[39] 田捷, 董迪, 秦承虎, 杨鑫. 一种基于螺旋扫描轨道的光学投影断层成像方法. 中国: CN102599887A, 2012-07-25.

[1] Mengjie Fang, Zipei Wang, Jie Tian, Di Dong. Predicting origin for bone metastatic cancer using deep learning-based pathology. EBIOMEDICINE[J]. 2023, 88: 104449-, http://dx.doi.org/10.1016/j.ebiom.2023.104449.
[2] ShuaiTong Zhang, SiYun Wang, Jie Zhang, Di Dong, Wei Mu, Xueer Xia, FangFang Fu, YaNan Lu, Shuo Wang, ZhenChao Tang, Peng Li, JinRong Qu, MeiYun Wang, Jie Tian, JianHua Liu. Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study. HELIYON[J]. 2023, 9(3): e14030-, http://dx.doi.org/10.1016/j.heliyon.2023.e14030.
[3] Liwen Zhang, Lianzhen Zhong, Cong Li, Wenjuan Zhang, Chaoen Hu, Di Dong, Zaiyi Liu, Junlin Zhou, Jie Tian. Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images. NEURAL NETWORKS[J]. 2022, 152: 394-406, doi.org/10.1016/j.neunet.2022.04.027.
[4] 操润楠, 方梦捷, 李海林, 田捷, 董迪. 半监督内镜图像长尾分类. 中国医学科学杂志:英文版[J]. 2022, 37(3): 171-180, http://lib.cqvip.com/Qikan/Article/Detail?id=7108258218.
[5] Mengjie Fang, Jie Tian, Di Dong. Non-invasively predicting response to neoadjuvant chemotherapy in gastric cancer via deep learning radiomics. ECLINICALMEDICINE[J]. 2022, 46: 101380-, [6] Lixin Gong, Min Xu, Mengjie Fang, Bingxi He, Hailin Li, Xiangming Fang, Di Dong, Jie Tian. The potential of prostate gland radiomic features in identifying the Gleason score. COMPUTERS IN BIOLOGY AND MEDICINE[J]. 2022, 144: 105318-, [7] Li, Cong, Qin, Yun, Zhang, WeiHan, Jiang, Hanyu, Song, Bin, Bashir, Mustafa R, Xu, Heng, Duan, Ting, Fang, Mengjie, Zhong, Lianzhen, Meng, Lingwei, Dong, Di, Hu, Zhenhua, Tian, Jie, Hu, JianKun. Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer. MEDICAL PHYSICS[J]. 2022, 49(3): 1535-1546, http://dx.doi.org/10.1002/mp.15437.
[8] Zhao, Xun, Liang, YuJing, Zhang, Xu, Wen, DongXiang, Fan, Wei, Tang, LinQuan, Dong, Di, Tian, Jie, Mai, HaiQiang. Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING[J]. 2022, 49: 2972-2982, [9] He, BingXi, Zhong, YiFan, Zhu, YongBei, Deng, JiaJun, Fang, MengJie, She, YunLang, Wang, TingTing, Yang, Yang, Sun, XiWen, Belluomini, Lorenzo, Watanabe, Satoshi, Dong, Di, Tian, Jie, Xie, Dong. 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. TRANSLATIONAL LUNG CANCER RESEARCH[J]. 2022, 11(4): 670-685, [10] Liwen Zhang, Di Dong, Yongqing Sun, Chaoen Hu, Congxin Sun, Qingqing Wu, Jie Tian. Development and validation of a deep learning model to screen for trisomy 21 during the first trimester from nuchal ultrasonographic images. JAMA NETWORK OPEN[J]. 2022, 5(6): e2217854-, [11] Wang, Siwen, Dong, Di, Li, Liang, Li, Hailin, Bai, Yan, Hu, Yahua, Huang, Yuanyi, Yu, Xiangrong, Liu, Sibin, Qiu, Xiaoming, Lu, Ligong, Wang, Meiyun, Zha, Yunfei, Tian, Jie. A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS[J]. 2021, 25(7): 2353-2362, http://apps.webofknowledge.com/CitedFullRecord.do?product=UA&colName=WOS&SID=5CCFccWmJJRAuMzNPjj&search_mode=CitedFullRecord&isickref=WOS:000678341200001.
[12] Tian, Panwen, He, Bingxi, Mu, Wei, Liu, Kunqin, Liu, Li, Zeng, Hao, Liu, Yujie, Jiang, Lili, Zhou, Ping, Huang, Zhipei, Dong, Di, Li, Weimin. Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images. THERANOSTICS[J]. 2021, 11(5): 2098-2107, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797686/.
[13] Zhang, Lu, Wu, Xiangjun, Liu, Jing, Zhang, Bin, Mo, Xiaokai, Chen, Qiuying, Fang, Jin, Wang, Fei, Li, Minmin, Chen, Zhuozhi, Liu, Shuyi, Chen, Luyan, You, Jingjing, Jin, Zhe, Tang, Binghang, Dong, Di, Zhang, Shuixing. MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma. JOURNAL OF MAGNETIC RESONANCE IMAGING[J]. 2021, 53(1): 167-178, http://dx.doi.org/10.1002/jmri.27308.
[14] Meng, Lingwei, Dong, Di, Chen, Xin, Fang, Mengjie, Wang, Rongpin, Li, Jing, Liu, Zaiyi, Tian, Jie. 2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-Center Study. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS[J]. 2021, 25(3): 755-763, https://www.webofscience.com/wos/woscc/full-record/WOS:000626521100015.
[15] Wang, Xiaoxiao, Li, Cong, Fang, Mengjie, Zhang, Liwen, Zhong, Lianzhen, Dong, Di, Tian, Jie, Shan, Xiuhong. Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer. BMC MEDICAL IMAGING[J]. 2021, 21(1): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989204/.
[16] Dong, Di, Tang, Zhenchao, Wang, Shuo, Hui, Hui, Gong, Lixin, Lu, Yao, Xue, Zhong, Liao, Hongen, Chen, Fang, Yang, Fan, Jin, Ronghua, Wang, Kun, Liu, Zhenyu, Wei, Jingwei, Mu, Wei, Zhang, Hui, Jiang, Jingying, Tian, Jie, Li, Hongjun. The Role of Imaging in the Detection and Management of COVID-19: A Review. IEEE REVIEWS IN BIOMEDICAL ENGINEERINGnull. 2021, 14: 16-29, http://apps.webofknowledge.com/CitedFullRecord.do?product=UA&colName=WOS&SID=5CCFccWmJJRAuMzNPjj&search_mode=CitedFullRecord&isickref=WOS:000708445300006.
[17] Wu, Xiangjun, Dong, Di, Zhang, Lu, Fang, Mengjie, Zhu, Yongbei, He, Bingxi, Ye, Zhaoxiang, Zhang, Minming, Zhang, Shuixing, Tian, Jie. Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study. MEDICAL PHYSICS[J]. 2021, 48(5): 2374-2385, http://dx.doi.org/10.1002/mp.14767.
[18] Hu, Hao, Gong, Lixin, Dong, Di, Zhu, Liang, Wang, Min, He, Jie, Shu, Lei, Cai, Yiling, Cai, Shilun, Su, Wei, Zhong, Yunshi, Li, Cong, Zhu, Yongbei, Fang, Mengjie, Zhong, Lianzhen, Yang, Xin, Zhou, Pinghong, Tian, Jie. Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study. GASTROINTESTINAL ENDOSCOPY[J]. 2021, 93(6): 1333-+, http://dx.doi.org/10.1016/j.gie.2020.11.014.
[19] Zhong, Lianzhen, Dong, Di, Fang, Xueliang, Zhang, Fan, Zhang, Ning, Zhang, Liwen, Fang, Mengjie, Jiang, Wei, Liang, Shaobo, Li, Cong, Liu, Yujia, Zhao, Xun, Cao, Runnan, Shan, Hong, Hu, Zhenhua, Ma, Jun, Tang, Linglong, Tian, Jie. A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study. EBIOMEDICINE[J]. 2021, 70: http://dx.doi.org/10.1016/j.ebiom.2021.103522.
[20] Sun, RuiJia, Fang, MengJie, Tang, Lei, Li, XiaoTing, Lu, QiaoYuan, Dong, Di, Tian, Jie, Sun, YingShi. CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer. EUROPEAN JOURNAL OF RADIOLOGY[J]. 2020, 132: http://dx.doi.org/10.1016/j.ejrad.2020.109277.
[21] Di Dong. Predicting response to immunotherapy in advanced non-small cell lung cancer using tumour mutational burden radiomic biomarker. Journal for ImmunoTherapy of Cancer (共同第一作者). 2020, [22] Zhong, Lianzhen, Dong, Di, Tang, Linglong, Han, Shuyan, Tian, Jie. Deep learning-based prognosis prediction in T3N1 nasopharyngeal carcinoma patients treated with induction chemotherapy followed by concurrent chemoradiotherapy. CANCER RESEARCH[J]. 2020, 80(16): http://apps.webofknowledge.com/CitedFullRecord.do?product=UA&colName=WOS&SID=5CCFccWmJJRAuMzNPjj&search_mode=CitedFullRecord&isickref=WOS:000590059301397.
[23] Chen, Jiaming, He, Bingxi, Dong, Di, Liu, Ping, Duan, Hui, Li, Weili, Li, Pengfei, Wang, Lu, Fan, Huijian, Wang, Siwen, Zhang, Liwen, Tian, Jie, Huang, Zhipei, Chen, Chunlin. Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma. BRITISH JOURNAL OF RADIOLOGY[J]. 2020, 93(1108): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362918/.
[24] Li, Jing, Dong, Di, Fang, Mengjie, Wang, Rui, Tian, Jie, Li, Hailiang, Gao, Jianbo. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. EUROPEAN RADIOLOGY[J]. 2020, 30(4): 10-, https://www.webofscience.com/wos/woscc/full-record/WOS:000507798400010.
[25] Li, Cong, Dong, Di, Li, Liang, Gong, Wei, Li, Xiaohu, Bai, Yan, Wang, Meiyun, Hu, Zhenhua, Zha, Yunfei, Tian, Jie. Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS[J]. 2020, 24(12): 3585-3594, http://dx.doi.org/10.1109/JBHI.2020.3036722.
[26] Hu, Wenchao, Wu, Xiangjun, Dong, Di, Cui, LongBiao, Jiang, Min, Zhang, Jibin, Wang, Yabin, Wang, Xinjiang, Gao, Lei, Tian, Jie, Cao, Feng. Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score. INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING[J]. 2020, 36(10): 2039-2050, http://dx.doi.org/10.1007/s10554-020-01896-4.
[27] 王睿, 李靖, 方梦捷, 董迪, 梁盼, 高剑波. 基于能谱CT的影像组学术前预测进展期胃癌淋巴结转移的价值. 中华医学杂志[J]. 2020, 100(21): 1617-1622, http://lib.cqvip.com/Qikan/Article/Detail?id=7102206832.
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（1） Radiomics and Its Clinical Application: Artificial Intelligence and Medical Big Data, Academic Press, 2021-06, 第 2 作者
（2） 影像组学基础, 科学出版社, 2022-04, 第 3 作者

   

（ 1 ） 针对小鼠全身成像的多模融合显微成像技术及应用研究, 主持, 国家级, 2016-01--2018-12
（ 2 ） 基于时空信息融合的植物根系多尺度原位观测系统研制, 主持, 部委级, 2016-01--2017-12
（ 3 ） 2017年中国科学院青年创新促进会, 主持, 部委级, 2017-01--2020-12
（ 4 ） 基于OPT成像系统的双模融合成像功能研发（中国科学院仪器设备功能开发技术创新项目）, 主持, 部委级, 2016-09--2017-08
（ 5 ） 基于影像组学的肝细胞癌预后预测的关键科学问题研究（中国科学院国际合作重点项目）, 主持, 部委级, 2017-01--2018-12
（ 6 ） 基于影像组学和大数据的肺癌筛查新技术研究（肺癌筛查和干预技术及方案研究）, 主持, 国家级, 2017-08--2020-07
（ 7 ） 基于分子影像和影像组学的乳腺癌早诊、疗效评价与预后预测新技术研发, 参与, 国家级, 2017-08--2021-07
（ 8 ） 基于影像组学的EGFR突变型晚期非小细胞肺癌靶向治疗疗效预测研究, 主持, 国家级, 2018-01--2021-12
（ 9 ） 基于多模态MRI影像组学的脑胶质瘤智能影像诊断研究, 主持, 省级, 2018-12--2021-12
（ 10 ） 基于多模态影像组学的胃癌新辅助化疗疗效预测研究, 主持, 国家级, 2020-01--2023-12
（ 11 ） 基于影像和病理融合的胃肠道肿瘤微卫星不稳定（MSI）评估及免疫治疗疗效预测, 主持, 国家级, 2020-01--2022-12
（ 12 ） 胃癌影像组学, 主持, 国家级, 2021-01--2023-12

已指导学生

吴相君  硕士研究生  085211-计算机技术  

现指导学生

操润楠  硕士研究生  081104-模式识别与智能系统  

刘瑜佳  硕士研究生  085208-电子与通信工程  

张广昊  硕士研究生  085208-电子与通信工程  

赵洵  硕士研究生  081104-模式识别与智能系统