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接标

发布日期:2018-03-05 浏览次数:4152 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。

主要从事机器学习、模式识别和医学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1项,安徽省自然科学研究面上项目1项,模式识别国家重点实验室项目1项(结题),参与多项国家基金项目,近3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20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. Bimedical Engineering》《Medical Image Analysis》等,以及多次在顶级国际会议(如:MICCAI等)发表论文。

 

研究方向

机器学习、模式识别、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:人工智能和数字图像处理

主要主持科研课题

[1].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

[2].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

[3].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, DOI: 10.1109/TIP.2018.2799706, 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. 2876-97, 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].    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. DOI: 10.1109/TMI.2018.2798500, 2018.

[10].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, doi:10.1007/s11682-017-9753-4. 2017.

[11].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

[12].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.

[13].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.

[14].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.

2.      会议论文

[1].    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, In MLMI 2018.

[2].    Biao Jie Xi Jiang, MingXia, Daoqiang Zhang. Sub-network Based Kernels for Brain Network Classification. In BrainKDD 2016..

[3].    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.

[4].    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

[5].    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

[6].    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)

[7].    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.

[8].    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.

[9].    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.

[10].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.

[11].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.

[12].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.

[13].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, ShenZhen, China, 2014.

 

授权专利

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

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