接标

发布人:王正东
发布日期:2018-03-05
浏览次数:1105

联系方式

电话:15655391919

Emailjbiao@nuaa.edu.cn 

个人简介

    接标,男,19776月出生,博士,副教授,硕士生导师。分别于20154月和20066月获得博士和硕士学位,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交流访问学习1年。主要从事机器学习和脑影像分析等领域的研究工作。目前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,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《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,模式识别与医学图像数据分析 

 

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, 78.2万元。

[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、2015/07-2017/068万元。

[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,2015/01-2016/12,5万元。 

 

代表性论文

1. 期刊论文

[1]. 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(SCI)

[2]. 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. (In press). (SCI)

[3]. 接标,张道强,面向脑网络的新型图核及其在MCI分类上的应用, 计算机学报. 39(8), 2016

[4]. 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. (SCI)

[5]. 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. (SCI)

[6]. 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. (SCI)

[7]. 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. (SCI)

[8]. 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. (SCI)

[9]. 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. 1-12: (SCI)

[10].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.

[11].接标,张道强. 基于网络拓扑特性的MCI分类,数据采集与处理,285): 602-6072013

[12].接标,汪金宝,凌宗虎,左开中. 基于区域相似性模型的图像检索研究。计算机工程与应用. 47(15)213-2152011

[13].费飞, 王立鹏, 接标, 张道强. 判别性子图挖掘方法及其在MCI分类中的应用. 南京大学学报:自然科学版, 2015(2).

[14].吴锦华, 左开中, 接标,. 新颖的判别性特征选择方法. 计算机应用, 2015, 35(10):2752-2756.

[15].乔云峰, 接标, 郭良敏, 罗永龙. P2P环境下基于模糊理论的Dirichlet信任评估模型 2015831. 计算机工程与应用, 2015.

[16].钱晓亮, 左开中, 接标. 新的基于Laplacian的特征选择方法 2015519. 计算机工程与应用, 2015.

[17].王立鹏,费飞,接标,张道强.基于子图选择和图核降维的脑网络分类方法。计算机科学与探索,20148(10)1246-1253

2. 会议论文:

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

[19].Fengjun Zhao, Yanrong Chen, Huangjian Yi, Xiaowei He, 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). (Accepted)

[20].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.

[21].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

[22].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

[23].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)

[24].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.

[25].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.

[26].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.

[27].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.

[28].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.

[29].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.