徐军
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  • 所在单位:人工智能学院、未来技术学院
  • 办公地点:人工智能学院亚培楼W203
  • 性别:
  • 联系方式:xujunus@gmail.com
  • 职称:教授
  • 毕业院校:浙江大学
  • 学科:控制科学与工程
  • 个人简介
  • 研究方向
  • 社会兼职
  • 教育经历
  • 工作经历
  • 团队成员
  • 其他联系方式

智慧医疗研究院(点击访问网站)

第九届医学图像计算青年研讨会(MICS2022)将于2022年7月8-10日在南京信息工程大学举行

学习与工作经历

2021-至今    南京信息工程大学人工智能学院(未来技术学院)(Link)      教授,副院长

2011-2021    南京信息工程大学自动化学院(Link)             教授

2014,2018,2019 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

研究领域

1.计算病理(Link)、数字病理(Link)医学影像计算(Link)

2.人工智能(Link)、机器学习(Link) 

3.面向癌症(癌症:众疾之王 之 1.灵丹妙药;2.盲人摸象;3.攻瑕蹈隙)计算机辅助诊断与预后的影像与病理图像计算(幻灯片下载:地址1地址2) (爱奇艺播放链接视频链接视频下载)

4.乳腺(Link)、前列腺(Link)、肺(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.高分辨率免疫荧光成像的多光谱病理图像计算(Related Link)

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

  近十年来主要致力运用先进的机器学习和数字图像处理方法,探索1)高分辨率数字化病理切片(显微镜图像)的像素层级、细胞层级、组织层级以及全景切片层级的图像计算的新方法与技术,2)影像图像的器官及感兴趣区域或目标的自动检测、识别和分割;3)构建新颖的亚视觉特征以俘获人眼难以或者无法区分的图像模式,从而接近或者超越人眼的视觉局限。同时量化影像和病理医生的经验和知识,构建影像和病理图像表型组定量化描述疾病;4)基于高分辨率免疫荧光成像的多光谱病理图像计算等,并致力于把这些新的方法和技术运用于1) 疾病的自动识别与诊断及亚型的分类;2)基于图像表型描述的疾病图像生物学标记的量化和表征;3) 疾病的转移风险、恶性程度的预测、预后以及患者对治疗的反应等疾病的自动检测、诊断、预测和预后系统,4)融合病理、影像、分子、基因与蛋白及临床文本数据等不同尺度的数据,拟从不同的维度定量地描述疾病信息。期望这些研究成果未来能够辅助医生实现疾病诊疗过程的标准化、定量化、客观化、可重复和高通量,从而辅助医生进一步改善疾病的预防、诊断及治疗,最终让患者获益。

  • 2020年第七届医学图像计算青年研讨会 (MICS)报告:计算病理及其数字化切片组织形态学定量分析(报告视频链接)


发表论文(Google Scholar CitationResearchGate)

标注*的作者为我指导的学生

2022年

1. Jiawei Xie*, Xiaohong Pu, Jian He, Yudong Qiu, Cheng Lu, Wei Gao*, Xiangxue Wang, Haoda Lu*, Jiong Shi, Yuemei Xu, Anant Madabhushi, Xiangshan Fan, Jun Chen, Jun Xu, Survival Prediction on Intrahepatic Cholangiocarcinoma with Histomorphological Analysis on the Whole Slide Images, Computers in Biology and Medicine, 2022 (Link)

2. Shi Liang*, Haoda Lu*, Min Zang, Xiangxue Wang, Yiping Jiao, Tingting Zhao, Eugene Yujun Xu, Jun Xu, Deep SED-Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules, Cytometry Part A, 2022 (Link)

3. Zelin Zhang*, Xianqi Huang, Qi Yan,Yani Lin, Enbin Liu, Yingchang Mi, Shi Liang*, Hao Wang, Jun Xu, Kun Ru, The diagnosis of chronic myeloid leukemia with deep adversarial learning, The American Journal of Pathology, 2022. (Link)

4. Shen Zhao, Chao-Yang Yan*, Hong Lv, Jing-Cheng Yang, Chao You, ZiAng Li*, Ding Ma, Yi  Xiao, Jia Hu, Wen-Tao Yang, Yi-Zhou Jiang, Jun Xu, and Zhi-Ming Shao, "Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer", Fundamental Research, 2022. (Link)

5.余力*,刘宵雪,闫朝阳*,李建瑞,张志强,黄韫栀,徐军面向多模态MRI脑胶质瘤区域三维分割与生存期预测的级联U-Net网络, 中国图象图形学报,vol. 27 (3), 2022.(Link)

2021年

1.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 MRIMagnetic Resonance in Medicine, 00:1–15, 2021.(Link)

2. Yun Bian, Cong Liu*,Yinghao Meng, Fang Liu, Kai Cao, Hao Zhang, Xu Fang, Jing Li, Jieyu Yu, Xiaochen Feng, Chao Ma, Jianping Lu, Jun Xu, Chengwei Shao, “Preoperative Radiomics Approach to Evaluating Tumor-Infiltrating CD8+ T Cells in Patients with Pancreatic Ductal Adenocarcinoma Using Non-contrast Magnetic Resonance Imaging”, Journal of Magnetic Resonance Imaging, 2021. (Link)

3. 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)

4.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)

5.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

6.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) : 851-8552021.  (Link)

7.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, vol. 70, 101835, 2021. (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, vol. 194, 2020. (Link)

3. 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)

4. 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)

5. 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)

6. 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)

7. Daqiu Li, Zhangjie Fu, and Jun Xu, Stacked-autoencoder-based model for COVID-19 diagnosis on  CT images, Applied Intelligence, vol.363, 2020 (Link)

8. 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)

9. 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)

10. 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)

11. 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", 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://ssl1230a75e822c6f3334851117f8769a30e1c.vpn.nuist.edu.cn/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,2019.(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)。

6.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*, 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)

3. 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)

4.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)

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, 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 to the paper)

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: 1–122017 (Link to the paper).


2016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 paperDatasetsMICS2015 Spotlight from 21:46) 700+次引用, ISI高被引论文)

2.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. (Link to the paper) (400+次引用, ISI高被引论文)

3. 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)

4. 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)

5. 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*, 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]

2. 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

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

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, 5(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 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 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, 2009.700+次引用, ISI高被引论文) (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 International Symposium on Biomedical Imaging: From Nano to Macro, June 28-July 1, 2009, Boston, Massachussetts, USA. (Link to the paper)

11.   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 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)


科研项目

1.国家自然科学基金-浙江两化融合基金重点资助项目: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(U1809205), 2019.01-2022.12(主持)

2.国家自然科学基金面上项目:基于生精管组织形态定量分析的精子发育分期与雄性不育研究(No.62171230),2022.01-2025.12 (主持)

3.国家自然科学基金重大研究计划集成项目:肿瘤演进与诊疗的分子功能可视化研究:基于影像和病理组学多模态信息融合的三阴性乳腺癌关键分子可视化与诊疗新策略(No.92159301),2022.01-2025.12 (参与)

4.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)

5.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)

6.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)

7.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 (主持,结题)

8.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,结题)

9. 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,结题)

10.2015江苏省双创团队人才计划 (2015.06-2018.06) (核心成员)


学术报告

2022

1.面向乳腺疾病的全栈式人工智能辅助诊疗和预后系统,2021年第十三届上海市乳腺癌专业研讨会暨圣安东尼奥乳腺癌会议集锦2022年1月1日,上海。

2.计算病理及其在疾病预后和生殖医学领域的研究进展,“医学人工智能”前沿论坛--致敬一线抗疫的医学专家,2022年4月29,线上会议(Link)

3.计算病理及其对疾病辅助诊疗和预后的价值,山东省保健科技协会第一届口腔颌面部肿瘤精准治疗高峰论坛,2022年5月29,线上会议

4.计算病理:从图像计算通往精准医学之路,智慧诊断医学系列讲座:第一期之计算病理学沙龙,2022年6月2,线上会议

2021

1.影像组学和深度机器学习:图像计算对精准医学的价值,第十二届中国放射青年医师学术论坛,2021年5月29-30,北京,线上会议(Link)

2.面向疾病精准诊疗和预后的影像和病理图像计算,吴文俊人工智能科学技术奖十周年颁奖盛典暨2020中国人工智能智能产业年会2021411(Link)

3.人工智能、影像和病理图像计算:它们对精准医学的价值,第四届中国精准医学大会202177-9日,重庆。(Link)

4.计算病理及其在泌尿疾病辅助诊疗中的价值,2021浦江前列腺癌高峰论坛-转化医学论坛, 2021710日,上海。

5. 影像和病理图像计算及其对疾病精准诊疗的价值,中华医学会2021年全国结核病学术大会2021721-25日,成都。(Link)

6.Parallel Radiological and Pathological Image Analysis for Precision Medicine, 2021 IEEE International Conference on Digital Twins and Parallel Intelligence, Section: Parallel Images, July 29, 2021. (Link)

7. 人工智能、影像和病理图像计算及对精准医学的价值, 2021827日,广州。

8. 影像和病理图像计算:它们对精准医学的价值,2021智慧教育与虚拟实验论坛2021425日,佛山。

9. 影像和病理组学及其对精准医学的贡献, 2021年医学人工智能大会(CMAI2021),中国科学院大学, 2021925, 北京(线上)(Link)

10. 人工智能,影像和病理组学:它们对精准医学的价值,2021第二届申江医学影像论坛2021929日,上海.

11. 影像和病理图像计算及对疾病精准预防、诊疗和预后的价值, 杭州电子科技大学人工智能研究院20211014日,杭州(线上)

12. 人工智能,影像和病理图像计算:它们对精准医学的价值,2021江苏省放射学会腹部组年会2021117日,南京(线上)

13.计算病理及其在非肿瘤病理中的研究价值,中国医疗保健国际交流促进会病理学分会2021年会暨新一代病理学高峰论坛,2021年12月11日,广州 (线上)。

14. 人工智能赋能病理诊疗和预后: 基本原理和应用,2021第七届中国数字病理论坛暨中华医学会数字病理与人工智能工作委员会第3次会议,20211212  (线上)

15. 影像和病理组学:图像和精准医学之间的桥梁,第五届图像计算与数字医学国际研讨会(ISICDM2021)医工交叉论坛,2021年12月19日  (线上)

16.人工智能赋能病理诊疗和预后基本原理和应用,四川省肿瘤学会人工智能与大数据专委会2021年会,四川省国际医学交流促进会精确放射治疗专业委员会成立大会暨第一次会议,成都20211225  (线上)

17.人工智能、影像和病理图像计算及其对精准医学的价值,2021浙江省数字经济产业发展大会暨第三届中国长三角数字经济大会智慧健康与数智助老论坛,2021年12月30日  (线上)

2020

1.计算病理及其数字化切片组织形态学定量分析,2020年第七届医学图像计算青年研讨会 (MICS),大连,2020年7月18-19日(报告视频链接)

2.  影像和病理图像计算:它们在乳腺精准诊疗中的价值,2020第十二届上海市乳腺专业研讨会,上海, 2020年12月26日。

3.  影像和病理图像计算:它们对精准医学的价值,第十届上海泌尿肿瘤国际论坛-转化医学论坛,上海,2020年12月19日。

4.计算病理及其在胃肠疾病的研究进展与展望,肿瘤病理专委会-胃肠肿瘤学组系列学术活动,在线,2020年12月19日(Link)。

5. 影像和病理图像计算:它们对精准医学的价值,第二届医工融合学术研讨会(在线),山东大学,2020年12月12-13日.

6.  影像和病理图像计算:它们对精准医学的价值,第四届图像计算与数字医学国际研讨会,沈阳,2020年12月5-7日(Link)

7.  计算病理与组织形态学定量分析:它们在非癌症病理中的研究价值,2020年中国生物医学工程年会暨创新医疗峰会-医学图像与人工智能,北京,2020年11月20-21日(Link)

8.  影像和病理图像计算:它们对精准医学的价值,2020浙江大学医学院第二附属医院广济学术周,杭州,2020年11月21日。

9.  影像和病理图像计算:它们对精准医学的价值,国际测试委员会智慧医疗科技大会&中国医学人工智能大会,青岛,2020年10月31日. (Link)

10.  影像和病理图像计算:它们对精准医学的价值,中国工业与应用数学学会第十八届年会:数理医学的理论进展与应用成果,长沙,2020年10月31日.

11. "Computational Pathology and Histomorphological Analysis for Precision Medicine", MICCAI2020 Challenges on Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology, Oct. 8, 2020 (Link).

12.影像和病理图像计算:它们对精准医学的价值,中北大学信息学院,太原,2020年10月28日.

13.计算病理与数字化切片组织形态学定量分析:它们对精准医学的价值,2020年中国医疗保健国际交流促进会病理分会年会,苏州,2020年9月18-20日。

2019

1.Computational Pathology for Precision Medicine, MICCAI2019 Workshop on Computational Pathology for Precision Medicine, Shenzhen, Oct. 13, 2019. (Link)

2. Computational Pathology: from Digital Pathology to Precision Medicinethe 5th Digital Pathology Congress: Asia, April 3rd-4th, 2019, Tokyo, Japan.

3.Computational Pathology for Precision Medicine, French-Chinese Seminar on ICT for Health, 22 April 2019, Southeast University, Nanjing.

4.Computational Pathology: the opportunities and challenges, the 4th Digital Pathology Congress: Asia, May 9th-10th, 2018, Tokyo, Japan.

5.影像和病理图像计算及其对精准医学的价值,中山大学计算机系,广州,2019年10月12日。

6.从数字病理到计算病理:它们对精准医学的价值,2019年10月10日,第十三届中国医师协会病理科医师年会,合肥。

7.面向乳腺癌伴随诊断的影像和病理图像计算,2019年乳腺肿瘤诊疗先进技术跨学科研讨会复旦大学附属肿瘤医院,2019年7月28日。

8.计算病理及肾脏穿刺APOL1突变风险预测,中国医学装备协会病理装备分会,2019年7月19日,苏州。

9.计算病理:从数字病理到精准医学,2019中国医疗保健国际促进会病理分会年会,2019年5月18-19日,福州。

2018年及以前

1.影像和病理组学:图像与精准医学之间的桥梁,2018人工智能与医疗健康论坛,南京医科大学生物医学工程与信息学院,2018年12月23日,南京 (Link

2.影像和病理组学及其对精准医学的贡献,首届中国医学影像AI大会,2018年12月16日, 上海 (Link)。

3.影像和病理组学及其对精准医学的贡献,2018年中国医学人工智能大会, 北京,中国科学院大学,2018年9月22-23日。

4.Computational Pathology for precision medicine, 17th Annual Meeting of Japanese Society of Digital Pathology (17JSDP), August 31st- September 2nd, 2018, Kure, Japan.

5.医学图像计算及其对精准医学的贡献,第六届脑长寿高峰论坛,南京,2018年6月29-30日。

6.影像和病理组学及其对精准医学的贡献, 2018年中国生物医学工程学会联合学术年会-医学成像与人工智能分论坛, 深圳,2018年9月20-22日

7.乳腺癌辅助诊断–人工智能如何为医生赋能,2018杭州第三届国际乳腺癌论坛,杭州,2018年7月28-29日。

8.计算病理学及其对精准医学的贡献--人工智能如何为医生赋能,第四届中国医疗保健国际交流促进会病理学年会暨数字病理和人工智能的应用大会,长沙,2018年5月19-20日(Link

9.计算病理学及其对精准医学的贡献,2018开放医疗与健康联盟第三届年会:医疗人工智能的前沿进展,杭州,2018年1月20日(Link)

10.计算病理学,2017中国人工智能产业年会医疗人工智能专题论坛,苏州,2017年12月24日(Link)

11.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,第7分会场G205, 15:25-15:50, 2017年10月28日(Link)

12.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,2017年9月24日(Link)(讲座视频链接

13.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)

14.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,2017年5月13日(Link)

15.面向癌症诊断和预后的组织病理图像深度计算,第14期临床病理联盟直播课堂,(Link)

16.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;2016年10月19日-10月22日(Link)


教 学


1. 机器学习                     本科生         2014-2022 秋学期

2. 机器学习                     研究生         2012-2020 春学期

3. 机器学习:从入门到沉迷            本科生全校通识课   2015    春学期



1.计算病理、数字病理; 2. 医学图像计算;影像和常规数字病理切片的定量分析; 3.计算机视觉、机器学习; 4. 面向癌症计算机辅助诊断与预后的病理图像分析; 5. 乳腺、前列腺、肺、头颈部等...

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