接标 - 安徽师范大学 - 计算机与信息学院

个人简介

接标，男，1977年生，博士，教授，博士生导师。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)的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。

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

[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. DOI.org/10.1016/ j.media.2020.101709. [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. [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, May 2018. [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. [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. [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. [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 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. [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] 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) , 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) , 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]. 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

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