2011-至今 南京信息工程大学信息与控制学院(Link) 教授 2014,2018 美国凯斯西储大学生物医学工程系(Link) 访问教授 2008-2011 美国Rutgers大学生物医学工程系(Link) 博士后,助理研究员 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

1. 国家自然科学基金-浙江两化融合基金重点资助项目(No.U1809205)：基于医学图像深度计算的乳腺癌新辅助化疗病理缓解程度评估和预测, 2019.01-2022.12 (主持) 2. 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) 3. 江苏省自然科学基金面上项目：基于淋巴结病理图像的乳腺癌自动分期系统(No. BK20181411），2018.07-2021.06 (主持) 4. 国家自然科学基金面上项目：基于影像-病理组学对胰腺癌精准诊断及预后评估的研究(No. 81871352)，2019.01-2022.12 (参与) 5. 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) 6. 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）(结题) 7. 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，(结题) 8. 2015江苏省双创团队人才计划，2015.06-2018.06， (核心成员)

2020年 1. Jun Xu, Haoda Lu*, Haixin Li, Chaoyang Yan*, Xiangxue Wang, Ming Zang, Rooij D.G. de Dirk, Anant Madabhushi, and Eugene Yujun Xu, “Computerized Spermatogenesis Staging (CSS) of Testis Sections for Mouse Sperm Development via Quantitative Histomorphological Analysis, Medical Image Analysis, 2020.(Under minor revision) (Link) 2. Chaoyang Yan*, Kazuaki Nakane, Xiangxue Wang, Yao Fu, Haoda Lu*, Xiangshan Fan, Michael D. Feldman, Anant Madabhushi, Jun Xu, “Automated Gleason Grading on Prostate Biopsy Slides by Statistical Representations of Homology”, Computer Methods and Programs in Biomedicine, 2020. (Under minor revision) (Link) 3. Yun Bian, Zengrui Zhao*, Hui Jiang, Xu Fang, Jin Li, Kai Cao, Chao Ma, Li Wang, Shiwei Guo, Li Wang, Jin Gang, Jianping Lu, Jun Xu, ,“Non-Contrast Radiomics Approach for Predicting Grades of Non-functional Pancreatic Neuroendocrine Tumors”, Journal of Magnetic Resonance Imaging, 2020.(Link) 4. Zengrui Zhao*, Yun Bian, Hui Jiang, Xu Fang, Jin Li, Kai Cao, Chao Ma, Li Wang, Jianming Zheng, Xiaodong Yue, Huiran Zhang, Xiangxue Wang, Anant Madabhushi, Jun Xu, Jin Gang, and Jianping Lu, "CT-radiomic approach to predict G1/2 non-functional pancreatic neuroendocrine tumor",Academic Radiology, 2020. (Link) 5. Chaoyang Yan*, Jun Xu, Jiawei Xie*, Chengfei Cai*, Haoda Lu*，“Prior-aware CNN with Multi-Task Learning for Colon Images Analysis”, International Symposium on Biomedical Imaging 2020 (ISBI2020), April 3-7, 2020, Iowa City, Iowa, USA (Link) (Oral Presentation) 6. Sara Arabyarmohammadi, Zelin Zhang*, Patrick Leo, Marjan Firouznia, Andrew Janowczyk, Haojia Li, Nathaniel M. Braman, Kaustav Bera, Behtash Nezami, Jun Xu, Leland Metheny, Anant Madabhushi, “Computationally derived image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation” , SPIE on Medical Imaging, Digital Pathology , Houston, Texas, USA, February 15-20, 2020 (Link) 7. Can Koyuncu, Cheng Lu, Zelin Zhang*, Pingfu Fu, Dibson D Gondim, Jun Xu,, Kaustav Bera, James S. Lewis, Anant Madabhushi, "Tumor Cell Multinucleation Is More Frequent in African-American Oropharyngeal Squamous Cell Carcinoma Patients Than Caucasian-American Ones – Implications for Outcome Differences", United States and Canadian Academy of Pathology's 109th Annual Meeting, Los Angeles, California, February 29th-March 5, 2020. (Link) 8. Sara ArabYarmohammadi, Marjan Firouznia, Zelin Zhang*, Patrick Leo, Andrew R Janowczyk,Kaustav Bera, Behtash Ghazi Nezami, Howard J. Meyerson, Jun Xu,, Leland Metheny, Anant Madabhushi, "COMPUTATIONALLY Derived Fractal Features of Blasts from Aspirates Smears to Predict Relapse in Acute Myeloid Leukemia Patients Following Allogenic Hematopoietic Stem Cell Transplant", United States and Canadian Academy of Pathology's 109th Annual Meeting, Los Angeles, California, February 29th-March 5, 2020. (Link) 2019 年 1. Jun Xu, Chengfei Cai*, Yangshu Zhou, Bo Yao, Xiangxue Wang, Zhihong Zhang, Ke Zhao, Anant Madabhushi, Zaiyi Liu, Li Liang, "Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Deeptissue Net", 15th European Congress on Digital Pathology, April 11-13th, 2019 (Oral Presentation) (Link) 2. Jun Xu, Haoda Lu*, Haixin Li, Xiangxue Wang, Anant Madabhushi, Yujun Xu, "Histopathological Image Analysis on Mouse Testes for Automated Staging of Mouse Seminiferous Tubule", 15th European Congress on Digital Pathology, April 11-13th, 2019. (Link) 3. Jun Xu, Lei Gong*, Guanhao Wang*, Cheng Lu, Hannah Gilmore, Shaoting Zhang, and Anant Madabhushi，“A Convolutional Neural Network initialized Active Contour Model with Adaptive Ellipse Fitting (CoNNACaeF) for Nuclear Segmentation on Breast Histopathological Images”, Journal of Medical Imaging, 6(1), 017501 (2019). https://doi.org/10.1117/1.JMI.6.1.017501(Link) 4. 谢嘉伟*，陈骏，徐军，樊祥山， 基于肝内胆管癌全景病理切片定量分析的生存预测， 中华医学会病理学分会第二十五次学术会议暨第九届中国病理年会（获优秀论文奖）(Link)。 5. 李宝明*，胡佳瑞*，徐海俊*，吴海玲*，朱涵*，顾家瑞*，王聪，蒋燕妮，张智弘，徐军，基于深度级联网络的乳腺淋巴结全景图像癌转移区域的自动识别，2019中国生物医学工程大会（获青年优秀论文竞赛三等奖）(Link)。 2018年 1. Jun Xu, Andrew Janowczyk, Laura M. Barisoni,, Chengfei Cai*, Jeffrey Nirschl, Matthew Palmer, Michael D. Feldman, D Chen, John O’Toole, Z Zaky, Emilio Poggio, John R. Sedor, and Anant Madabhushi, "Predicting APOL1 risk category from kidney donor biopsies using deep learning", American Society of Nephrology (ASN) Kidney Week 2018 (Oral Presentation)(Link) 2. Lewis, JS, Zhang, Z*, Xu, J, Lu, C, Bishop, J, Madabhushi, A, “Computerized Quantitation of Tumor Cell Multinucleation is Strongly Prognostic for p16-Positive Oropharyngeal Squamous Cell Carcinoma”, United States and Canadian Academy of Pathology's 108th Annual Meeting, National Harbor, MD, March 16th-21st, 2019.(Link) 3. 孙明建*，徐军，马伟*，张玉东，“基于新型深度全卷积网络的肝脏CT影像三维区域自动分割”，中国生物医学工程学报，vol. 37，issue (4): 385-393， 2018. (Link to the paper) 4. 马伟*，刘鸿利，孙明建*，徐军，蒋燕妮，新型乳腺磁共振增强图像肿瘤区域 的自动分割模型, 中国生物医学工程学报, 2018(录用) 2017年 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，2017年04月20日-22日，北京。（口头报告,获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 Imaging, 4(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: 1–12，2017 (Link to the paper). 2016年 1. Jun Xu, Xiaofei Luo*, Guanhao Wang*, Hannah Gilmore, Anant Madabhushi, “A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial and Stromal Regions in Histopathological Images”, Neurocomputing, volume 191, pp.214-223, 2016. (Link to the paper)[PDF]( ISI高被引论文) 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，2017, 43(11): 2003-2013. (Link to the paper) 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，2017, 36(3): 276-283. (Link to the paper) 2015年 1. Jun Xu, Lei 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 Imaging, vol. 35, issue 1, pp. 119-130, 2016. (Link to the paper, Datasets, MICS2015 Spotlight from 21:46) [PDF] ( ISI高被引论文) 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. 龚磊*，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 Letters, volume 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.1227-1230，2009 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