徐军

 

基本信息

性  别:
出生年月:

民  族:

汉族

电  话:

025-5869-9833

办公时间:

E - mail:

jxu@nuist.edu.cn

专  业: 控制科学与工程-模式识别与智能系统
职  务:
   
主页网址:

办公地点:

学科楼3号楼C311

工作单位:

自动化系

讲授课程:

机器学习(研究生),机器学习与模式识别(本科生)

招生方向:

欢迎自动化、数学、计算机、电子工程等专业学生报考

学  院: 信息与控制学院
职  称: 教授/高级
   
教育与工作经历

2011-至今  南京信息工程大学信息与控制学院(Link)   教授

2014      美国凯斯西储大学生物医学工程系(Link)   访问助理教授

2008-2011  美国Rutgers大学生物医学工程系(Link)    博士后,助理研究员

2007-2008  美国Alcorn州立大学先进技术系        博士后

2004-2007  浙江大学控制科学与工程系(Link)       博士研究生

2001-2004  电子科技大学应用数学学院(Link)       硕士研究生

学术与社会兼职

中国生物医学工程协会生物信息与控制分会委员;江苏省计算机学会/江苏省微电脑应用协会,人工智能专委会委员;中国计算机协会会员;IEEE 会员

 

研究领域与方向

Hot 欢迎参加2017医学影像信息处理研讨会(MIIP)暨第三届长三角地区医学影像分析研讨会(免注册费)1021820-1710,南京信息工程大学滨江楼报告厅(会议网站)


研究领域介绍(by Anant Madabhushi教授@CWRU on Youku and Youtube)


1.计算病理学(Related Link)数字病理(Link)

2. 面向癌症计算机辅助诊断与预后的病理图像分析(幻灯片下载:地址1地址2) (爱奇播放链接视频链接视频下载)

3.计算机视觉(Related Link)、机器学习(Related Link我学生郎彬的CSDN博客)

4. 乳腺(Link)、前列腺(Link)Link头颈部(Link)癌症的计算机辅助检测、诊断及预后(Related Link)

5. 深度学习及大数据驱动的医学数据分析(Related Link)

6. 高分辨率、超光谱医学图像及遥感图像分析(Related Link)

主要项目、论文、专著和专利

标注*的作者为我指导的学生


A. 论文(Google Scholar CitationResearchGate)

2017

1. 蔡程飞*徐军,梁莉,魏建华,“基于深度卷积网络的结直肠全扫描病理图像多种组织分割”,2017年中国生物医学工程大会, 医学影像大数据分析分会20170420-22日,北京。(口头报告,2017中国生物医学工程大会“青年论文竞赛二等奖”(Link) 
2. Jun Xu, James P. Monaco, Rachel Sparks, Anant Madabhushi, “Connecting Markov Random Fields and Active Contour Models: Application to Gland Segmentation and Classification”, Journal of Medical Imaging4(2), 021107, 2017 (Link to the paper)
3. Jun Xu, Chao Zhou*, Bing Lang*, and Qingshan Liu, “Deep Learning for  Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers”Book Chapter: Deep Learning and Convolutional Neural Networks for Medical Imaging Computing, Editors: Le Lv, Yefeng Zheng, Gustavo Carneiro, Lin Yang, Springer, 2017. (Link to the book) (Cover) (In Press)

4. Jiamei Chen, Yan Li, Jun Xu, Lei Gong*, Linwei Wang, Wenlou Liu, Jingping Yuan, Qingming Xiang, Qunhua Zheng, Juan Liu, “Computer-aided Prognosis on Breast Cancer with Hematoxylin & Eosin Histopathology Images: A Review”, Tumor Biology, March 2017: 1–122017 (Link to the paper).


2016

1. Jun XuXiaofei Luo*, Guanhao Wang*, Hannah Gilmore, and Anant Madabhushi,"A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial and Stromal Regions in Histopathological Images", Neurocomputingvolume 191, pp.214-223, 2016 (Link to the paper)[PDF]

2. Cheng Lu, Hongming Xu, Jun Xu, Hannah Gilmore, Mrinal Mandal, and Anant Madabhushi, “Multi-Pass Adaptive Voting for Nuclei Detection in Histopathlogical Images”, Scientific Reports 6: 33985, 2016. (Link to the paper)

3. 骆小飞*徐军,陈佳梅,“基于逐像素点深度卷积网络分割模型的上皮和间质组织分割”,自动化学报2016 (录用) .(Link to the journal)

4. 周超*徐军,罗波, 基于深度卷积神经网络和结合策略的乳腺组织病理图像细胞异型性自动评分”, 中国生物医学工程学报2016 (录用). (Link to the journal)


2015

1.  Jun XuLei Xiang*, Qingshan Liu, Hannah Gilmore, Jianzhong Wu, Jinghai Tang, and Anant Madabhushi,"Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology images", IEEE Trans. on Medical Imagingvol. 35, issue 1, pp. 119-130, 2016. (Link to the paper, Datasets, MICS2015 Spotlight from 21:46)[PDF]

2.  Jun Xu, Lei Xiang*, Guanhao Wang*, Shridar Ganesan, Michael Feldman, Natalie NC Shih, Hannah Gilmore, and Anant Madabhushi, “Sparse Non-negative Matrix Factorization (SNMF) based Color Unmixing for Breast Histopathological Image Analysis”, Computerized Medical Imaging and Graphics, vol. 46, pp.20-29, 2015. (Link to the paper)[PDF]

3. Xiaofan Zhang, Hang Dou, Tao Ju, Jun Xu, Shaoting Zhang, “Fusing Heterogeneous Features from Stacked Sparse Autoencoder for Histopathological Image Analysis”, IEEE Journal of Biomedical and Health Informatics, 2015. (Link to the paper)

4. Angel Cruz-Roa, Jun Xu, Anant Madabhushi, “A note on the stability and discriminability of graph based features for classification problems in digital pathology”,  Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928703, 2015.  (Link to the paper[PDF]

5. *徐军,王冠皓*,吴建中,唐金海,“基于多特征描述的乳腺癌肿瘤病理自动分级”,计算机应用35(12): 3570-3575.Link to the paper

6. 王冠皓*徐军基于多级金字塔卷积神经网络的快速特征表示方法 计算机应用研究32(8): 2492-2495, 2015. (Link to the paper)


2014年  

1.  Jun Xu, Renlong Hang*, “A New Committee Based Active Learning Approach to Hyperspectral Remote Sensing Data Classification”, Remote Sensing Lettersvolume 5, issue 6, pp.511-520, 2014. (Link to the paper)

2.  Jun Xu, Renlong Hang*, and Qingshan Liu, “Patch-based Active Learning (PTAL) for Spectral-Spatial Classification on Hyperspectral Data”, International Journal of Remote Sensing, volume 35, issue 5, pp. 1846-1875, 2014. (Link to the paper)

3.  Jun Xu, Lei Xiang*, Renlong Hang*, Jiangzhong Wu, “Stacked Sparse Autoencoder (SSAE) based Framework for Nuclei Patch Classification on Breast Cancer Histopathology”2014 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, April 29-May 2, 2014, Beijing, China, pp. 999 - 1002. (Oral Presentation) (Link to the paper)

4.  郑秋中*徐军 “一种基于群稀疏特征选择的图像检索方法”,计算机应用研究31(9): 2867-2872, 2014. (Link to the paper)

5.  曹冬梅*徐军 “基于先验形状的混杂活动边界模型及其在图像分割中的应用计算机科学41(11):301-305,316, 2014. (Link to the paper)

6.  Mengjie Mei*, Jun Xu, “Shape Sharing Initialized Active Contour Model for Image Segmentation”, Chinese Control Conference 2014, Nanjing, July 28-30, pp. 4791 – 4796, 2014. (Oral Presentation) (Link to the paper)

7. 王冠皓*徐军,“基于群稀疏理论的乳腺动态对比度增强核磁共振图像联合重建”,计算机应用34(11):3304-3308, 2014. (Link to the paper)


2013年及以前论文

1.    Shannon C. Agner, Jun Xu, and Anant Madabhushi, “Spectral Embedding based Active Contour (SEAC) for Lesion Segmentation on Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging”, Medical Physics, vol. 40, 032305, 2013.(2013年第3期封面论文) (Link to the paper)

2.    Jun Xu, Andrew Janowczyk, Sharat Chandran, and Anant Madabhushi, “A High-throughput Active Contour Scheme for Segmentation of Histopathological Imagery”, Medical Image Analysis, 15(6):851-862, 2011. (Line to the paper)

3.    Shannon C. Agner, Jun Xu, Mark Rosen, Sarah Englander and Anant Madabhushi, “Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI”, 2011 SPIE Symposium on Medical Imaging, February 12-17, Florida, USA, 2011. (Link to the paper)

4.    Ajay Basavanhallya, Elaine Yua, Jun Xu, Shridar Ganesan, Michael Feldman, John Tomaszewski, Anant Madabhushi, "Incorporating Domain Knowledge for Tubule Detection in Breast Histopathology Using O'allaghan Neighborhoods", 2011 SPIE Symposium on Medical Imaging, February 12-17, Florida, USA, 2011. (Link to the paper)

5.    Hussain Fatakdawala, Jun Xu, Ajay Basavanhally, Anant Madabhushi, Gyan Bhanot, Shridar Ganesan, Michael Feldman and John Tomaszewski, “Expectation Maximization driven Geodesic Active Contour with Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology", IEEE Trans. on Biomedical Engineering, vol. 57, pp.1676-1689, 2010. (Link to the paper)

6.    Jun Xu, James Monaco and Anant Madabhushi, “Markov Random Field driven Region-based Active Contour Model (MaRACel): Application to Medical Image Segmentation", MICCAI2010:the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6363(Pt 3), pp 197-204, 2010. (Link to the paper)

7.    Jun Xu, Andrew Janowcyzk, Sharat Chandran, Anant Madabhushi, "A Weighted Mean Shift, Normalized Cuts Initialized Color Gradient Based Geodesic Active Contour Model: Applications to Histopathology Image Segmentation", SPIE Symposium on Medical Imaging, vol.7623, San Diego, USA, 2010. (Link to the paper)

8.    Jun Xu, Rachel Sparks, Andrew Janowcyzk, John E. Tomaszewski, Michael D. Feldman, and Anant Madabhushi, "High-throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Needle Core Biopsies", Workshop on Prostate Cancer Imaging: the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, Beijing, China, LNCS 6367, pp. 77-88, 2010. (Link to the paper)

9.    Jinshan Tang, Rangaraj M. Rangayyan, Jun Xu, Issam El Naqa, and Yongyi Yang, “Computer-Aided Detection and Diagnosis of Breast Cancer with Mammography: Recent Advances," IEEE Trans. on Information Technology in Biomedicine, vol. 13, no. 2, pp.236-251, 2009. (Link to the paper)

10.  Shannon C. Agner, Jun Xu, Anant Madabhushi, Sarah Englander and Mark Rosen, “Quantitative DCE-MRI Signatures of Triple Negative Breast Cancer: A Computer-Aided Diagnosis Framework", pp.1227-12302009 IEEE Internaitonal Symposium on Biomedical Imaging: From Nano to Macro, June 28-July 1, 2009, Boston, Massachussetts, USA. (Link to the paper)

11.   A. Basavanhally, Jun Xu, S. Ganesan and A. Madabhushi, “Computer-aided  prognosis(CAP) of ER+breast cancer histolopathology and correlating survival outcome with Oncotype DX assay”, pp.855-858, 2009 IEEE Internaitonal Symposium on Biomedical Imaging: From Nano to Macro, June 28-July 1,2009, Boston, Massachussetts, USA. (Link to the paper)

12.  Hussain Fatakdawala, Ajay Basavanhally, Jun Xu, Anant Madabhushi, Gyan Bhanot, Shridar Ganesan, Michael Feldman and John Tomaszewski, “Expectation Maximization driven Geodesic Active Contour with Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology",pp.69-76, 9th IEEE International Conference on BioInformatics and BioEngineering, June 22-24, 2009, Taiwan, China. (Link to the paper)

13.  Jun Xu, Yong-Yan Cao, Youxian Sun and Jinshan Tang, “Absolute Exponential Stability of Recurrent Neural Networks with Generalized Activation Function”, IEEE Trans. on Neural Networks, vol.19, no.6, pp.1075-1089, 2008. (Link to the paper)

14.  Jun Xu, Yong-Yan Cao, Daoying Pi and Youxian Sun, “An estimation of the domain of attraction for general recurrent delayed neural networks", Neurocomputing, vol.71, no.7-9,pp.1566-1577, 2008. (Link to the paper)

15.  Jun Xu and Jinshan Tang, “Detection of Clustered Microcalcifications Using An Improved Texture Based Approach for Computer Aided Breast Cancer Diagnosis System," Computer Society of India Communications (CSI Communications), pp. 17-20, vol 31, issue 10, January 2008. (Link to the paper)

16.   Jun Xu, Daoying Pi, Yong-Yan Cao, “Delay-independent and delay-dependent Stability of a novel delayed neural networks", Dynamics of Continuous, Discrete and Impulsive Systems, Series B, vol. 15, pp. 791-806,2008. (Link to the paper)

17.  伍世虔,徐军动态模糊神经网络设计与应用,清华大学出版社,2008. (Link to the book)

18.  Jun Xu, Daoying Pi , Yong-Yan Cao and Shouming Zhong, “On stability of neural networks by a Lyapunov functional based approach", IEEE Trans. on Circuits and Systems-I: Regular Paper, vol.54, no.4, pp.912-924, 2007. (Link to the paper)


B.  科研项目

1. 国家自然科学基金面上项目:基于放射-病理组学的乳腺癌转移风险预测模型研究(No. 61771249),2018.01-2021.12 (主持)

2. 国家自然科学基金面上项目:基于病理图像的雌激素受体阳性乳腺癌复发风险预测研究(No.61273259),2012.01-2016.12 (结题)

2. 江苏省“六大人才高峰”高层次人才项目资助计划:基于乳腺DCE-MR图像的肿瘤类型自动诊断系统(2013-XXRJ-019(主持)

3. 江苏省自然科学基金面上项目:基于钼靶图像的乳腺癌检测与诊断决策支持系统研究BK20141482), 2014.07-2017.06,(主持)

4.   2015江苏省双创团队人才计划,2015.06-2018.06, (核心成员) 


C. 硕士生毕业论文


1. 骆小飞,基于病理图像的上皮和间质组织自动分割2016年;

2. 龚磊,基于病理图像的乳腺肿瘤定量化分析2016年;

3. 项磊,基于计算机辅助预后的乳腺病理图像分析”, 2015年校级优秀硕士论文;

4. 王冠皓,深度卷积网络及其在乳腺病理图像分析中的应用”, 2015年;

5. 梅梦杰,主动轮廓模型及其在前列腺癌病理分级中的应用”,2015年;

6. 杭仁龙,基于主动学习的遥感图像分类研究,获2014年校级优秀硕士论文



获奖情况


其它学术成果

A. 学术报告:
1. 计算病理学,中华医学会病理学分会第二十三次学术会议暨第七届中国病理年会,苏州7分会场G205, 15:25-15:50, 20171028(Link)
2.“计算病理学:研究机遇与挑战”,2017图像计算与数字医学国际研讨会数字病理分会,成都2017924(Link)

3. Deep computing for digital pathology: toward computer-aided diagnosis and prognosis on cancers, the 3rd Digital Pathology Congress: Asia,  September 16th-17th, 2017, Guangzhou, China (Link)

4. “面向癌症诊断和预后的组织病理图像深度计算”,第三届中国数字化病理高峰论坛西安,2017513(Link)
5. 面向癌症诊断和预后的组织病理图像深度计算,14期临床病理联盟直播课堂(在线报告链接)
6. 面向癌症计算机辅助诊断和预后的深度计算,2016年中国计算机大会医疗大数据分论坛,山西太原20161019-1022(Link)
7. 面向癌症计算机辅助诊断与预后的病理图像分析,中国生物医学工程学会第九次会员代表大会暨2015年学术会议“医学图像信息与控制分会”报告2015124-7日。
8.Computer-aided Diagnosis and Prognosis on Breast Cancer第一届医学图像青年研讨会(MICS2014),深圳,20141115(Link)

B. 担任了如下期刊和会议的同行审稿人

•IEEE Trans. on Medical Imaging (Link)

IEEE Trans. on Biomedical Engineering(Link);

IEEE Trans. on Image Processing(Link);

IEEE Journal of Biomedical and Health Informatics(Link);

•IEEE Trans. on Neural Networks(Link);

•IEEE Trans. on Circuits and Systems-I: Regular Paper(Link);

IEEE Trans. on Systems, Man and Cybernetics, Part A (Link);

•IEEE Geoscience and Remote Sensing Letters (Link);

Bioinformatics (Link);
• Tumor Biology

• PLOS One (Link);

Computerized Medical Imaging and Graphics(Link);

•Pattern Recognition (Link);

•Physics Letters A(Link);

•Neurocomputing(Link);

• Biomedical Signal Processing & Control(Link);

•Signal Processing(Link);

•Journal of Selected Topics in Signal Processing(Link);

•Journal of Selected Topics in Applied Earth Observations and Remote Sensing(Link);

• Earth Observation and Geoinformation (Link);

•Machine Vision and Applications(Link);

•Circuits, Systems and Signal Processing(Link);

•Asian Journal of Control (Link);

Ultrasonic Imaging (Link);

•Journal of Medical Imaging (Link);

生物医学工程学杂志(Link);

自动化学报(Link);

•the 14 th--16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2011-2013)(Link);

•the 2007--2016 International Joint Conference on Neural Networks

(IJCNN2007--IJCNN2016)(Link);

•the 17th International Federation of Automatic Control World Congress (IFAC2007);

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