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接标

发布日期:2018-03-05 浏览次数: 发布人:王正东

个人简介:  接标,男,1977年生,博士,教授,博士生导师。分别于20154月和20066月获得博士和硕士学位,2017年和2012年在美国北卡罗来纳大学教堂山分校(UNC)生物医学研究影像中心分别从事博士后研究1年和交流访问1年。

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主要从事人工智能、机器学习、数据挖掘和图像分析等领域的研究工作。主持国家自然科学研究面上项目2项,省部级项目2项,近几年在国际期刊、会议和国内核心期刊上发表或录用SCI/EI论文30余篇。部分论文以第一作者发表在领域内重要国际期刊,如《IEEE Trans. Image Processing》、《Human Brain Mapping 》、《IEEE Trans. Biomedical Engineering》《Medical Image Analysis》等,以及多次在顶级国际会议(如:MICCAI等)发表论文。担任安徽省人工智能学会理事,人工智能学会认知专委会委员,中国图学学会图学大数据专业委员会委员,安徽省计算机学会青工委委员。担任IJCAI 2019IJCAI 2018AAAI2018AAAI2017等国际会议的PC Member,以及Human Brain MappingInformation Science和计算机学报、自动化学报等国内外期刊的审稿人。

研究方向

人工智能、机器学习、数据挖掘和图像分析

主讲课程

本科生:数据挖掘、软件测试

研究生:人工智能、数字图像处理

主持科研课题

[1].   国家自然科学基金面上项目(61976006-面向功能磁共振成像的动态脑网络分析及应用研究, 2020/01-2023/12

[2].   国家自然科学基金面上项目(61573023-基于机器学习的脑网络分析及其应用研究, 2016/01-2019/12

[3].   安徽省自然科学基金面上项目(1508085MF125-脑网络分析中图学习及其应用、2015/07-2017/06

[4].   模式识别国家重点实验室开放课题(201407361-基于机器学习的脑网络分析及其应用研究,2015/01-2016/12

 

代表性论文

1.     期刊论文

[1].    Biao Jie, Mingxia Liu, Dinggang Shen. “Intergration of Temporal and Spatial Properties of Dynamic Connectivity Networks for Automatic Diagnosis of Brain Disease”. Medical Image Analysis, 47:81-94, 2018.

[2].    Biao Jie, Mingxia Liu, Daoqiang Zhang, Dinggang Shen. “Sub-network Kernels for Connectivity Networks in Brain Disease Classification“. IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2340-2353, May 2018.

[3].   Biao Jie, Daoqiang Zhang, Jun Liu, Dinggang Shen, Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer’s Disease. IEEE Trans. Biomedical Engineering, vol. 64, No. 1, pp. 238-249, Jan., 2017.

[4].   Biao Jie, Dinggang Shen, Daoqiang Zhang. Hyper-Connectivity of Functional Networks for Brain Disease Diagnosis, Medical Image Analysis. vol. 32, pp. 84-100, Mar 24 2016.

[5].   Biao Jie, Daoqiang Zhang, The Novel Graph Kernel for Brain Networks With Application to MCI Classification, Chinese Journal of Computers. 39(8), 2016:1667-1680.

[6].   Biao   Jie, Daoqiang Zhang, Bo Cheng, Dinggang Shen: Manifold Regularized Multi-task   Feature Learning for Multi-modality Disease Classification. Human Brain   Mapping. 2015, 36(2):489-507.

[7].   Biao   Jie, Daoqiang Zhang, Chong-Yaw Wee, Dinggang Shen: Topological graph kernel   on multiple thresholded functional connectivity networks for mild cognitive   impairment classification. Human Brain Mapping, vol. 35, No. 7, pp.   2876-2897, Jul 2014.

[8].   Biao   Jie, Daoqiang Zhang, Wei Gao, Qian Wang, Chong-Yaw Wee, Dinggang Shen:   Integration of Network Topological and Connectivity Properties for   Neuroimaging Classification. IEEE Trans. Biomedical Engineering. Vol. 61, No.   2, pp. 576-589, 2014.

[9].   Mi Wang,   Biao Jie*, Weixin Bian, Xintao Ding, Wen Zhou, ZhengDong Wang, Mingxia Liu.   Graph-Kernel Based Sructured Feature Selection for Brain Disease   Classification Using Functional Connectivity Networks. IEEE Access. Vol.7,   pp.35001-35011, 2019.

[10].Yang LiJingyu LiuXinqiang Gao, Biao JieMinjeong KimPew-Thian YapChong-Yaw Wee, Dinggang Shen. Multimodal   hyper-connectivity of functional networks using functionally-weighted LASSO   for MCI classification. Medical Image Analysis Vol 52, pp.80-96, 2019.

[11].Daoqiang   Zhang, Jiashuang Huang, Biao Jie, Junqiang Du, Liyang Tu, Mingxia Liu.   “Ordinal Pattern: A New Network Descriptor for Brain Connectivity Networks“.   IEEE Transactions on Medical Imaging. Vol 37, no. 7, pp. 1711-1722, July   2018.

[12].Daoqiang   Zhang, Liyang Tu, Long-Jiang Zhang, Biao Jie, Guang-Ming Lu. Subnetwork   mining on functional connectivity network for classification of minimal   hepatic encephalopathy. Brain Imaging and Behavior, vol 12, pp 901-911, 2017.

[13].Guiyin   Hu, Yonglong Luo, Xintao Ding, Liangmin Guo, Biao Jie, Xiaoyao Zheng, Guorong   Cai. Alignment of grid points. Optik - International Journal for Light and   Electron Optics, Vol. 131, pp. 279-286, 2017.

[14].Zu Chen,   Biao Jie, MingXia Liu, Daoqiang Zhang. Label-aligned multi-task feature   learning for multimodal classification of Alzheimer's disease and mild   cognitive impairment. Brain Imaging & Behavior, PP. 1148-1159, 2016

[15].Xintao   Ding, Yonglong Luo*, Yunyun Yi, Biao Jie, Taochun Wang, Weixin Bian.   Orthogonal optimization for SIFT descriptor. Journal of Electronic Imaging, vol.   25 No.5, 2016

[16].Yang Li,   Chong-Yaw Wee, Biao Jie, Ziwen Peng, Dinggang Shen: Sparse Multivariate   Autoregressive Modeling for Mild Cognitive Impairment Classification.   Neuroinformatics, vol. 12, pp. 455-69, Jul 2014.

[17].Tingting   Ye, Zu Chen, Biao Jie, Daoqiang Zhang. Discriminative multi-task feature   selection for multi-modality classification of Alzheimer's disease. Brain   Imaging & Behavior, PP.1-11, 2015.

[18].Fei Fei,   Biao Jie, Daoqiang Zhang: Frequent and Discriminative Subnetwork Mining for   Mild Cognitive Impairment Classification, Brain Connectivity, vol. 4, pp.   347-60, Jun 2014.

[19]. Xintao   Ding, Kun Wang, Biao Jie, Yonglong Luo, Zhenhua Hu, and Jie Tian. Probability   method for Cerenkov luminescence tomography based on conformance error   minimization. Biomedical Optics Express, Vol. 5, No.7, pp. 2091-2112, 2014.

2.     会议论文

[1].  Zhengdong Wang, Biao Jie*, Weixin Bian, DaoQiang Zhang, Mingxia Liu. Adptive Thresholding of Functional Connectivity Networks for fMRI-based Brain Disease Analysis. In: Workshop Graph Learning in Medical Imaging(GLMI), Medical Image Computing and Computer Assisted Intervention (MICCAI) , Accepted. ShenZhen, China,Oct. 13-17, 2019.

[2].  Zhengdong Wang, Biao Jie*, Mi Wang, Chunxiang Feng, Wen Zhou, Mingxia Liu, Dinggang Shen. Graph-kernel-based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification. In: Workshop Graph Learning in Medical Imaging(GLMI), Medical Image Computing and Computer Assisted Intervention (MICCAI) , Accepted. ShenZhen, China, Oct. 13-17, 2019.

[3].  Biao Jie, Mingxia Liu, Chunfeng Lian, Feng Shi, Dinggang Shen Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis. Machine Learning in Medical Imaging(MLMI), vol 11046, Granada, Spain, Sep. 16-20, 2018.

[4].  Biao Jie Xi Jiang, MingXia, Daoqiang Zhang. Sub-network Based Kernels for Brain Network Classification. BrainKDD, Seattle, WA, USA, Oct. 02-05, 2016.

[5].   Biao Jie, Xi Jiang, Chen Zu, Daoqiang Zhang: The New Graph Kernels on Connectivity Networks for Identication of MCI. In: 4th Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner (MLINI). Advances in Neural Information Processing Systems (NIPS), Montreal, Quebec, Canada, Dec. 12 –13, 2014.

[6].  Biao Jie, Dinggang Shen, Daoqiang Zhang: Brain connectivity hyper-network for MCI classification. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 724-732. Boston, USA, Sep. 14-18, 2014.Student travel award

[7].  Biao Jie, Daoqiang Zhang, Bo Cheng, Dinggang Shen: Manifold regularized multi-task feature selection for multi-modality classification. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp.275-283. Nagoya, Japan, Sep. 22-26, 2013.Student travel award

[8].  Biao Jie, Daoqiang Zhang, Chong-Yaw Wee, Heung-Il Suk, and Dinggang Shen: Integrating multiple network properties for MCI identification. In: Workshop on Machine Learning in Medical Imaging (MLMI), Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 9-16. Nagoya, Japan, Sep. 22-26, 2013. (Oral)

[9].  Biao Jie, Daoqiang Zhang, Chong-Yaw Wee, Dinggang Shen: Structural feature selection for connectivity network-based MCI diagnosis. In: Workshop on Multimodal Brain Image Analysis (MBIA), Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 175-184. Nice, France, Oct. 1-5, 2012.

[10].  Yang Li, Xinqiang Gao, Biao Jie, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen . “Multimodal Hyper-Connectivity Networks for MCI Classification”, MICCAI 2017, Quebec, Canada, Sep. 10-14, 2017.

[11].  Chong-Yaw Wee, Yang Li, Biao Jie, Zi-wen Peng, and Dinggang Shen: Identification of MCI using optimal sparse MAR modeled effective connectivity networks. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 319-327. Nagoya, Japan, Sep. 22-26, 2013.

[12].  Tingting Ye, Zu Chen, Biao Jie, Daoqiang Zhang. Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification. Pattern Recognition in NeuroImaging (PRNI), 2015 International Workshop on. IEEE, 2015:45-48.

[13].  Bo Cheng, Daoqiang Zhang, Biao Jie, Dinggang Shen: Sparse multimodal manifold- regularized transfer learning for MCI conversion prediction. In: Workshop on Machine Learning in Medical Imaging (MLMI), Medical Image Computing and Computer Assisted Intervention (MICCAI), Nagoya, Japan, Sep. 22-26, 2013.

[14].  Fei Fei, LiPeng Wang, Biao Jie, Daoqiang Zhang: Discriminative Subnetwork Mining for Multiple Thresholded Connectivity-Networks-Based Classification of Mild Cognitive Impairment. In: International Workshop on Pattern Recognition in Neuroimaging (PRNI), Tübingen, Germany, June 4-6, 2014.

[15]. Lipeng Wang, Fei FeiBiao Jie, Daoqiang Zhang. Combining Multiple Network Features for Mild Cognitive Impairment Classification. In: The IEEE ICDM Workshop on Data Mining in Medical Imaging, Vol. 1, Pages: 996-1003, ShenZhen, China, 2014.1

发明专利

[1].   接标,左开中,王涛春,丁新涛,胡桂银,罗永龙一种基于Laplacian算子的特征选择方法:中国.ZL.201410713386.0。(授权)

[2].   接标,王咪, 卞维新,丁新涛,左开中,方群,罗永龙. 一种面向脑网络的结构化特征选择方法: 201810818259.5(实审)

[3].   接标,王正东,卞维新,丁新涛,周文,左开中,陈付龙,罗永龙. 一种基于权值分布的阈值化方法: 201910452319.0 (实审)

[4].   接标,王正东,王咪,卞维新,丁新涛,周文,左开中,陈付龙,罗永龙. 面向功能性脑网络的多阈值下基于多任务的特征选择方法: 201910591933.5 (受理)

联系方式

通信地址:安徽芜湖九华南路189号安徽师范大学计算机与信息学院 241002

E-mailjbiao@nuaa.edu.cn