BOB官方体育APP

BOB官方体育APP: BOB官方体育APP

联系我们 师大首页

BOB官方体育APP:

BOB官方体育APP:

BOB官方体育APP: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。

http://210.45.192.123:8080/_vsl/BF012B04CBED39D1D646E5DC525BA301/5D302D42/1D50B?e=.png

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1th International Workshop on Graph Learning in Medical Imaging (GLMI 2019)co-chair,IJCAI 2019,IJCAI 2018,AAAI2018,AAAI2017等国际会议的PC Member,以及Human Brain Mapping、Information 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:人工智能、数据挖掘和软件测试

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。(在研)

[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。已结题

[3]. 安徽省高校优秀青年人才支持计划项目(gxyqZD2017010-智能脑影像分析及其在疾病诊断中的应用2017/01-2019/12。已结题

[4]. 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。已结题

[5]. 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。已结题

 

代表性论文

1. 期刊论文

[1].    Biao Jie, Mingxia   Liu, Chunfeng Lian, Feng Shi, Dinggang Shen. “Designing Weighted Correlation   Kernels in Convolutional Neural Networks for Functional Connectivity based   Brain Disease Diagnosis”. Medical Image Analysis, 2020. vol. 63, pp.101709:   1-14. July, 2020. ISSN: 1361-8415

[2].    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.

[3].    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, 2018.04.04.

[4].    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, 2018.01.30. ISSN:1057-7149

[5].     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. ISSN:0018-9294

[6].    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.

[7].    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. ISSN:0254-4164

[8].    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. ISSN:1065-9471

[9].    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.

[10]. 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.

[11]. Yang LiJingyu LiuXinqiang GaoBiao 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.

[12]. 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.

[13]. 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.

[14]. 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,   2017,131(2): 279-286.

[15]. 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.

[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.

 


2. 会议论文

[1] Chunxiang Feng, Biao   Jie*, Xintao Ding, Daoqiang Zhang, and Mingxia Liu. Constructing   High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI   for Brain Dementia Identification, In: Machine Learning in Medical Imaging   (MLMI), Medical Image Computing and Computer Assisted Intervention (MICCAI),   pp. 303-311. Lima, Peru, Oct. 4-8, 2020. (Online, Oral)

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

[3] 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: Graph   Learning in Medical Imaging(GLMI), Medical Image Computing and Computer   Assisted Intervention (MICCAI) , ShenZhen, China, Oct. 13-17, 2019.

[4]. 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: Machine Learning in Medical Imaging(MLMI), Medical   Image Computing and Computer Assisted Intervention (MICCAI), vol 11046, Granada, Spain, Sep. 16-20, 2018.

[5]. 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]. Fengjun Zhao,   Yanrong Chen, Huangjian Yi, Xiaowei He, and Biao Jie*. Vessel   Extraction by Graph Cut method based on Centerline Estimation. In the 8th   International Conference on Internet Multimedia Computing and Service (ICIMCS   2016) Xi??an, Shanxi, China, August 19-21, 2016.

[11]. 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.

[12]   Mingxia Liu, Junqiang Du, Biao Jie, Daoqiang Zhang: Ordinal Patterns   for Connectivity Networks in Brain Disease Diagnosis. In: International   Conference on Medical Image Computing and Computer Assisted Intervention   (MICCAI’2016), pp1-9, Athens, Greece, Oct.17-21, 2016.

[13]. 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.

[14].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.

[15].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.

[16].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.

[17].Lipeng Wang, Fei   Fei,Biao 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

 

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 (授权)

[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 (授权)

 

主持教研/横向项目:

[1].    安徽省一流本科人才示范引领基地:计算机科学与技术一流本科人才示范引领基地(2019rcsfjd018),2020.01-2021.12。

[2].    安徽省级质量工程教学研究项目:面向人工智能方向创新性人才培养模式的研究2019jyxm0097,2020.01-2021.12。

[3].    安徽省六卓越、一拔尖卓越人才培养创新项目:计算机科学与技术卓越工程师教育培养计划(2018zygc059),2019.01-2020.12。

[4].    大规模在线开放课程(MOOC)示范项目-软件测试(2017mooc149),2018.01-2019.12。

[5].    赛尔网络下一代互联网技术创新项目,NGII20190612,2019.12-2020.12,联合主持(研究生,冯春香)

 

联系方式

通信地址:安徽芜湖九华南路189号BOB官方体育APP 241002。

E-mail:jbiao@nuaa.edu.cn。

 

BOB官方体育APP-b0b综合体育下载