AIM

2022

• Jiawei Xie, Xiaohong Pu, Jian He, et al. Survival prediction on intrahepatic cholangiocarcinoma with histomorphological analysis on the whole slide images[J]. Computers in Biology and Medicine, 2022.（Link）
• Shi Liang, Haoda Lu, Min Zang, et al. Deep SED‐Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules[J]. Cytometry Part A, 2022.（Link）
• Xiangxue Wang, Cristian Barrera, Kaustav Bera, et al. Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors[J]. Science Advances, vol. 8, 22, 2022.（Link）
• Yiping Jiao, Junhong Li, Shuming Fei. Staining condition visualization in digital histopathological whole-slide images[J]. Multimedia Tools and Applications, 81 (13), 17831-17847.（Link）
• Haoran Dou, Luyi Han, Yushang He, et al. Localiizing the recurrent laryngeal Nerve via Ultrasound with a Bayesian Shape Framework [Early accepted for MICCAI 2022].
• Yunkui Pang, Xu Chen, Yunzhi Huang, et al. Weakly supervised MR-TRUS Image Synthesis for Brachytherapy of Prostate Cancer [Early accepted for MICCAI 2022].

2021

• Bian Y, Liu C, Li Q, et al. Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8+ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging[J]. Journal of Magnetic Resonance Imaging, 2021.（Link）
• Cong Liu*, Yun Bian, Yinghao Meng, Fang Liu, Kai Cao, Hao Zhang, Xu Fang, Jing Li, MD, Jieyu Yu, Xiaochen Feng, Chao Ma, Jianping Lu, Jun Xu, Chengwei Shao, Preoperative prediction of G1 and G2/3 grades in patients with non-functional pancreatic neuroendocrine tumors using multimodality imaging, Academic Radiology, 2021.（Link）
• 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, vol. 70, 101835, 2021.（Link）
• Can F. Koyuncu, Cheng Lu, Kaustav Bera, Zelin Zhang*, Jun Xu, Paula Andrea Toro Castaño, Germán Corredor, Deborah Chute, Pingfu Fu, Wade L. Thorstad, Farhoud Faraji, Justin A. Bishop, Mitra Mehrad, Patricia D. Castro, Andrew G. Sikora, Lester D. R. Thompson, R. D. Chernock, Krystle A. Lang Kuhs, Jingqin Luo, Vlad C. Sandulache, David J. Adelstein, Shlomo Koyfman, James S. Lewis Jr., Anant Madabhushi, "Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma: A multi-site validation study", The Journal of Clinical Investigation, 2021. (IF=14.81)（Link）
• Zhang Z, Li B, Xu J. Automatic three-dimensional segmentation of liver and tumors regions based on conditional generative adversarial networks[J]. Sheng wu yi xue Gong Cheng xue za zhi= Journal of Biomedical Engineering= Shengwu Yixue Gongchengxue Zazhi, 2021, 38(1): 80-88.（Link）
• Wang X, Bera K, Barrera C, et al. A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer[J]. EBioMedicine, 2021, 69: 103481.（Link）
• Lu C, Koyuncu C, Corredor G, et al. Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers[J]. Medical Image Analysis, 2021, 68: 101903.（Link）
• Chaoyang Yan*, Jing-Jing Lu, Kang Chen, Lei Wang*, Haoda Lu*, Li Yu*, Mengyan Sun, Jun Xu, Scale- and Slice- aware Net (S2aNet) for 3D segmentation of organs and musculoskeletal structures in pelvic MRI，Magnetic Resonance in Medicine, 00:1–15, 2021.（Link）
• 徐军,计算病理及其对精准医学的贡献和价值,中国人工智能学会通讯:智慧医疗专题，第11卷第9期:29-35,2021
• 顾松*,鲁浩达*,谢嘉伟*,陈骏,樊祥山,徐军,计算病理及其对精准医学的价值,中华病理学杂志,50(8) : 851-855, 2021.（Link）
• 徐春燕*，谢嘉伟*，杨春霞，蒋燕妮，张智弘，徐军, 基于病理穿刺全切片组织形态学分析的乳腺癌新辅助化疗疗效预测, 四川大学学报(医学版), 52(2): 279-285, 2021.（Link）

2020

• 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", vol. 27, no. 12, Academic Radiology, 2020.（Link）
• Lu C, Bera K, Wang X, et al. A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study[J]. The Lancet Digital Health, 2020, 2(11): e594-e606.（Link）
• Wang Y, Sun L, Wang H, et al. A DNN for Arrhythmia Prediction Based on ECG[C]//International Conference on Health Information Science. Springer, Cham, 2020: 146-153.（Link）
• 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, vol. 194, 2020. （Link）
• 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(Oral Presentation)（Link）
• Vaidya P, Bera K, Gupta A, et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction[J]. The Lancet Digital Health, 2020, 2(3): e116-e128.（Link）
• Cheng Lu, Kaustav Bera, Xiangxue Wang, Prateek Prasanna, Jun Xu, Andrew Janowczyk, Niha Beig, Michael Yang, Pingfu Fu, James Lewis, Humberto Choi, Ralph A Schmid, Sabina Berezowska, Kurt Schalper, David Rimm, Vamsidhar Velcheti, and Anant Madabhushi，``Prognostic Model based off Tumor Cellular Diversity Features Derived from H&E Tissue Images for Early Stage Non-Small Cell Lung Cancer: A Multi-site Retrospective Study", The Lancet Digital Health, vol. 2, no. 11, E594-606, 2020.（Link）
• Bo Li, Yang Wang, Hui Jiang, Baoming Li*, Xiaohan Shi, Suizhi Gao, Canrong Ni, Zelin Zhang, Shiwei Guo, Jun Xu, and Gang Jin, “Pros and Cons: High Proportion of Stromal Component Indicates Better Prognosis in Patients with Pancreatic Ductal Adenocarcinoma–A Research Based on the Evaluation of Whole-Mount Histological Slides”, Frontiers in Oncology, 2020, 10: 1472.（Link）
• 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, 52: 1124-1136, 2020.（Link）
• Daqiu Li, Zhangjie Fu, and Jun Xu, Stacked-autoencoder-based model for COVID-19 diagnosis on CT images, Applied Intelligence, vol.363, 2020（Link）
• 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）
• 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）
• 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

• Yan C, Cai C, Xie J, et al. Prior Consistent CNN with Multi-Task Learning for Colon Image Classification[J]. 2019.（Link）
• Jun Xu, Haoda Lu*, Haixin Li, Xiangxue Wang, Anant Madabhushi, Yujun Xu, "Histopathological image analysis on mouse testes", 15th European Congress on Digital Pathology, April 11-13th, 2019.（Link）
• 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）
• Lewis J, Zhang Z, Xu J, et al. Computerized Quantitation of Tumor Cell Multinucleation is Strongly Prognostic for p16-Positive Oropharyngeal Squamous Cell Carcinoma[C]//LABORATORY INVESTIGATION. 75 VARICK ST, 9TH FLR, NEW YORK, NY 10013-1917 USA: NATURE PUBLISHING GROUP, 2019, 99.
• 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).（Link）
• Wang X, Moh S, Hubbard A, et al. Case classification with tumor antigen presenting and TGF-β signaling biomarkers to predict anti-PD-1 outcome in GI tract tumors using automated quantitative fluorescence multiplex IHC[J]. 2019.（Link）
• Wang X, Barrera C, Lu C, et al. Computerized nuclear morphometric features from H&E slide images are prognostic of recurrence and predictive of added benefit of adjuvant chemotherapy in early stage non-small cell lung cancer[C]//LABORATORY INVESTIGATION. 75 VARICK ST, 9TH FLR, NEW YORK, NY 10013-1917 USA: NATURE PUBLISHING GROUP, 2019, 99.
• 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，2019.（获优秀论文奖）（Link）
• 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）
• 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,vol.38，issue (1):28-34,2019（Link）

2018

• 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）
• Lewis, JS, Zhang, Z*, Jun Xu, 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）
• Xiangping Xu, Jun Li, MengChu Zhou, Jun Xu, and Jinde Cao, “Accelerated Two-Stage Particle Swarm Optimization for Clustering Not-Well-Separated Data”, IEEE Trans on Systems, Man, and Cybernetics: Systems, vol.50, no.11, pp.4212 - 4223, 2018.（Link）
• 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）

2017

• 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, 36(5): 632-636. 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-04月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）
• 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）
• 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）
• 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）

2016

• 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.（650+次引用, ISI高被引论文)（Link）
• Jun Xu, Xiaofei Luo*, Guanhao Wang*, Hannah Gilmore, Anant Madabhushi, “A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial andStromal Regions in Histopathological Images”, Neurocomputing, volume 191, pp.214-223, 2016.(370+次引用， ISI高被引论文)（Link）
• 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）
• 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）
• 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）

2015

• 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）
• 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）
• 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, vol.20, no.9, pp. 1377 - 1383, 2016.（Link）

2014

• 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）
• Jun Xu, Renlong Hang*, and Qinshan Liu, “The 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）
• 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）

2013 And Before

• 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）
• Jun Xu, Andrew Janowczyk, Sharat Chandran, and Anant Madabhushi, “A High-throughput Active Contour Scheme for Segmentation of Histopathological Imagery”, Medical Image Analysis, 5(6):851-862, 2011.（Link）
• 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）
• Ajay Basavanhallya, Elaine Yu, 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）
• 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）
• 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）
• 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）
• 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）
• 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.（690+次引用, ISI高被引论文)（Link）
• 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 International Symposium on Biomedical Imaging: From Nano to Macro, June 28-July 1, 2009, Boston, Massachussetts, USA.（Link）
• Ajay Basavanhally, Jun Xu, Shridar Ganesan and Anant Madabhushi, “Computer-aided prognosis(CAP) of ER+breast cancer histolopathology and correlating survival outcome with Oncotype DX assay”, pp.855-858, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, June 28-July 1,2009, Boston, Massachussetts, USA. （Link）
• 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）
• 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）
• 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.
• 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）
• 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）
• 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.
• 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）