联系方式

电话：0553-5910351

E-mail：jbiao@ahnu.edu.cn

研究方向

人工智能、机器学习、数据挖掘和图像分析

个人简介

接标，男，1977年生，博士，教授，博士生导师，现任安徽师范大学计算机与信息学院副院长，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。分别于2015年4月和2006年6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40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》《IEEE Transactions on Medical Imaging 》、《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1th International Workshop on Graph Learning in Medical Imaging (GLMI 2019)的co-chair，IJCAI 2019，IJCAI 2018，AAAI2018，AAAI2017等国际会议的PC Member，以及IEEE Transactions on Medical Imaging、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。

主讲课程

本科生：人工智能、机器学习和数据挖掘

研究生：高级人工智能

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]. Zhengdong Wang, Biao Jie*, Chunxiang Feng, Taochun Wang, Weixin Bian, Xintao Ding, Wen Zhou, Mingxia Liu. Distribution-guided Network Thresholdingfor Functional Connectivity Analysis in fMRI-based Brain Disorder Identification, IEEE Journal of Biomedical and Health Informatics ,2022,26(4):1602 - 1613.

[2]. 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

[3]. 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.

[4]. 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.

[5]. 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

[6].  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

[7]. 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.

[8]. 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

[9]. 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

[10]. 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.

[11]. 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.

[12]. Huang, Jiashuang; Wang, Mingliang; Xu, Xijia; Jie, Biao; Zhang, Daoqiang. A novel node-level structure embedding and alignment representation of structural networks for brain disease analysis. Medical Image Analysis, 2020, 65: 101755. DOI: 10.1016/j.media.2020.101755.

[13]. Yang Li, Jingyu Liu, Xinqiang Gao, Biao Jie, Minjeong Kim, Pew-Thian Yap, Chong-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.

[14]. 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.

[15]. 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.

[16]. 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.

[17]. 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.

[18].  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.

[19]. 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.

[20]. 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.

[21]. 黄嘉爽, 接标, 丁卫平, 张道强. 脑网络分析方法及其应用. 数据采集与处理, 2021, 36(04): 648-663

2. 会议论文

[1] Kai Lin, Biao Jie*, Peng Dong, Xintao Ding, Weixin Bian, and Mingxia Liu. Extracting Sequential Features from Dynamic Connectivity Network with rs-fMRI Data for AD Classification.In MLMI 2021 held in conjunction with the 24nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, pp. 664–673, September 27th to October 1st, 2021.

[2] Peng Dong, Biao Jie, Lin Kai, Xintao Ding, Weixin Bian, and Mingxia Liu. Integration of Handcrafted and Embedded Features from Functional Connectivity Network with rs-fMRI for Brain Disease Classification. In MLMI 2021 held in conjunction with the 24nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, pp. 674–681, September 27th to October 1st, 2021 .

[3] 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), held in conjunction with the 23nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, pp. 303-311. Lima, Peru, Oct. 4-8, 2020. (Online, Oral)

[4] 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.

[5] 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.

[6] 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.

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

[8] 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.

[9] 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）

[10] 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）

[11] 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)

[12] 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.

[13] 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.

[14] 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.

[15] 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.

[16] 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.

[17] 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.

[18] 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.

[19] 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.

[20] 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）, 2018.5.4.

[2]. 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: 中国.ZL.201910452319.0 (授权)，2021.3.16

[3]. 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: 中国.ZL.201910591933.5, （授权）2021.6.15

[4]. 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: ZL.201810818259.5（授权）

[5]. 接标，王正东，卞维新，丁新涛，周文，左开中，陈付龙，罗永龙. 一种动态高阶脑网络分析方法: 202110263923.6 （实审）

[6]. 接标 董鹏 林凯 周文 丁新涛 卞维新 郑明 罗永龙. 一种基于卷积神经网络的静态脑网络特征提取方法和系统: 202111097531.3 （实审）

[7]. 接标，林凯，董鹏，吴康乐，丁新涛，卞维新，郑明，罗永龙. 基于卷积网络和长短期记忆网络的脑网络特征提取方法:202111077853.1 （实审）

[8]. 接标，胡瑞豹，冯春香，王正东，张剑龙，张启政，卞维新，丁新涛，陈付龙，罗永龙，一种目标行为异常检测的方法：（申请）

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）