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罗斯工程技术学院
Stocker Center
Athens, OH 45701

Jundong Liu

Jundong Liu
Associate Professor
Stocker Center 321A
Biomedical Engineering
科学计算和沉浸式技术中心
电气工程与计算机科学
liuj1@eagle1027.com
740.593.1603
Learning & Intelligent Systems Lab Website

Jundong Liu directs the Learning & newbb电子平台智能系统实验室(LiSL). 他是电子工程与计算机科学学院(EECS)的副教授，隶属于生物医学工程(BME)项目. 主要研究方向:机器学习, deep learning, reinforcement learning, 神经形态计算及其在医学图像分析中的newbb电子, human speech and languages, radar signal processing, 信息检索和视觉引导自主性.

Research Interests: 电气工程和计算机科学, medical image analysis, computer vision, computer graphics

All Degrees Earned: Ph.D., Computer Information Science and Engineering, University of Florida; M.S., Computer Science, Peking University; B.S., Computer Science, Wuhan University.

期刊文章，学术期刊(25)

  • Yue, Y., Baltes, M., Abuhajar, N., Sun, T., Karanth , A., Smith, C., Bihl, T., Liu, J. (2023). 脑图像分割的脉冲神经网络微调. 2023. Frontiers on Neuroscience; 17: http://www.frontiersin.org/articles/10.3389/fnins.2023.1267639/full.
  • Sun, T., Gong, S., Wang, Z., Smith, C., Wang, X., Xu, L., Liu, J. (2021). 通过语音表示提高时域语音增强网络的可理解性. IEEE Access.
  • Liu, J., Muturi, H., Khuder, S., Helal, R., Ghadieh, H., Ramakrishnan, S., Kaw, M., Lester, S., Al-Khudhair, A., Conran, P., Chin, K., Gatto-Weis, C., Najjar, S. (2020). 在Pten单倍体不足的雄性小鼠中，Ceacam1的缺失促进前列腺癌的进展.. Metabolism: clinical and experimental; 107: 154215.
  • Shi, B., Liu, J. (2018). 基于几何变换的kNN和svm的非线性度量学习. Neurocomputing; http://www.sciencedirect.com/science/article/pii/S092523121830910X.
  • Huang, L., Liu, J., Zhang, X., Sibley, K., Najjar, S., Lee, M., Wu, Q. (2018). 抑制蛋白精氨酸甲基转移酶5促进肝脏线粒体生物发生.. 28. The Journal of biological chemistry; 293: 10884-10894.
  • Chen, Y., Wang, Z., Smith, C., Liu, J. (2018). 基于顺序耦合卷积神经网络的脑肿瘤精确分割. Medical Physics.
  • Chen, Y., Wang, Z., Shi, B., Sun, T., Zhang, P., Smith, C., Liu, J. (2018). 自适应卷积神经网络在海马三维分割中的newbb电子 . IEEE图像处理学报.
  • Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. (2018). 基于四网格的阿尔茨海默病皮质下形状分析. IEEE生物医学与健康信息学杂志.
  • Wang, Z., Chen, Y., Smith, C., Liu, J. (2018). 基于多尺度神经网络的脑白质超强鲁棒检测. Medical Physics.
  • Zhang, P., Shi, B., Smith, C., Liu, J. (2017). 学习特征变换以改进半监督分类. Pattern Recognition.
  • Shi, B., Chen, Y., Zhang, P., Smith, C., Liu, J. (2017). 非线性特征变换与深度融合用于阿尔茨海默病分期分析. Frankfurt, D60486 Germany: Pattern Recognition; 63: pp. 487-498. http://www.sciencedirect.com/science/article/pii/S0031320316302916.
  • Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. 四网格坐标建模及其在神经图像中的newbb电子. 计算机图形医学成像.
  • Liu, J., Ramakrishnan, S., Khuder, S., Kaw, M., Muturi, H., Lester, S., Lee, S., Fedorova, L., Kim, A., Mohamed, I., Gatto-Weis, C., Eisenmann, K., Conran, P., Najjar, S. (2015). 高热量饮食加剧Pten肿瘤抑制基因单倍不足小鼠前列腺肿瘤的发生.. 3. Molecular metabolism; 4: 186-98.
  • Arum, O., Boparai, R., Saleh, J., Wang, F., Dirks, A., Turner, J., Kopchick, J., Liu, J., Khardori, R., Bartke, A. (2014). 生长激素受体基因破坏(GHR-KO)小鼠胰岛素敏感性的特异性抑制减弱了慢衰老的表型特征.. 6. Aging cell; 13: 981-1000.
  • Colvin, R., Liu, J. (2012). 2011年五大湖生物信息学会议记录. Preface. BMC Bioinformatics; 13 Suppl 2: I1.
  • Chen, X., Gu, X., Saiyin, H., Wan, B., Zhang, Y., Li , J., Wang, Y., Gao, R., Wang, Y., Dong, W., Najjar, S., Zhang, C., Ding, H., Liu, J., Yu, L. (2012). PCTAIRE1上的脑选择性激酶2 (BRSK2)磷酸化可负性调节胰腺β细胞中葡萄糖刺激的胰岛素分泌.. 36. The Journal of biological chemistry; 287: 30368-75.
  • Liu, J., Colvin, R. (2012). Preface. S-2. BMC Bioinformatics; 13: I1. http://dx.doi.org/10.1186/1471-2105-13-S2-I1.
  • Gupta, S., Yan, Y., Malhotra, D., Liu, J., Xie, Z., Najjar, S., Shapiro, J. (2012). 瓦巴因和胰岛素诱导肾上皮钠泵内吞作用.. 3. Hypertension (Dallas, Tex. : 1979); 59: 665-72.
  • Liu, J., Chelberg, D., Smith, C., Chebrolu, H. (2009). 基于局部似然的脑MR图像水平集分割方法. F09. International Journal of Tomography and Statistics; 12: http://www.ceser.in/ceserp/index.php/ijts/article/view/145.
  • Smith, C., Chebrolu, H., Markesbery, W., Liu, J. (2008). 改善症状前轻度认知障碍和阿尔茨海默病的预测模型. 10. Neurological Research; 30: 1091-1096. http://www.ncbi.nlm.nih.gov/pubmed/18768112.
  • Liu, J., wang, y. (2008). 脑形态分析的分割辅助图像配准. 5. International Journal of Computational Science; 2: 690-707.
  • Li, C., Liu, J., Fox, M. (2005). 蛇形自动初始化与分裂的外力场分割. 11. Pattern Recognition; 38: 1947-1960. http://dl.acm.org/citation.cfm?id=1746640.1746706.
  • Cao, L., Harrington, P., Liu, J. (2005). SIMPLISMA和ALS在化学战剂模拟物二维非线性小波压缩离子迁移谱中的newbb电子. 8. Analytic Chemistry; 77: 2575-2586. http://pubs.acs.org/doi/abs/10.1021/ac0486286.
  • Liu, J., Vemuri, B., Bova, F. (2002). 使用局部频率映射的高效多模态图像配准. 3. Secaucus, NJ: Machine Vision and Application/Springer-Verlag New York Inc.; 13: 149-163. http://www.springerlink.com/content/abea06g00yvtyqu3/.
  • Liu, J., Vemuri, B., Marroquin, J. (2002). 鲁棒多模态图像配准的局部频率表示. 5. IEEE Transactions on Medical Imaging; 21: 462-469. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1009382.

Patent (1)

  • Zhu, J., Wilhelm, J., Williams II, R., Uijt de Haag, M., Bartone, C., Liu, J., Chelberg, D., Liu, C., DiBenedetto, M. An Integrated, Scalable All-Weather, All-Terrain, All-Time, 自主周界监测和地面检查系统, Provisional patent application. OU16018.

Book, Chapter in Scholarly Book (5)

  • Liu, J. (2011). 脑MR图像的分割辅助配准. Springer Science ; http://www.springer.com/us/book/9781441982032.
  • Liu, J. (2008). 分割辅助图像配准的统一框架. 14. 计算科学新进展，Jorgensen/ Shen/Shu/Yan主编. / World Scientific; 1: 243-254.
  • Liu, J., Wang, Y. (2008). 分割辅助图像配准的统一框架. World Scientific; 243-254.
  • Liu, J. (2007). 基于变形模型的图像配准. Springer; 1: 517-542.
  • Liu, J. (2007). 15. 可变形模型:生物医学和临床newbb电子，Suri/Farag编辑.,; 1: 517-542.

Conference Proceeding (65)

  • Song, S., Qin, X., Brengman, J., Bartone, C., Liu, J. (2023). 基于地表地图和Yolo网络的整体FOD检测. 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP); 1-6. http://ieeexplore-ieee-org.proxy.library.eagle1027.com/document/10285910 .
  • Zhang, Y., Liu, J. (2023). 基于顶点的网络加速路径规划算法. . 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP); 1-6.
  • Yue, Y., Baltes, M., Abuhajar, N., Smith, C., Bihl, T., Liu, J. (2023). 混合脉冲神经网络对海马分割的微调. 雅典:IEEE国际生物医学成像研讨会(ISBI'23); http://biomedicalimaging.org/2019/.
  • Baltes, M., Smith, C., Liu, J. (2023). 基于ANN-SNN联合训练的目标定位与图像分割. IEEE声学、语音和信号处理国际会议(ICASSP'23).
  • Liao, B., Chen, Y., Wang, Z., Smith, C., Liu, J. (2022). A Comparative Study on 1.通过深度神经网络模型进行5T - 3T MRI转换. IEEE机器学习与newbb电子国际会议(ICMLA'22).
  • Sun, T., Gong, S., Wang, Z., Smith, C., Wang, X., Xu, L., Liu, J., Abuhajar, N. (2022). 个性化条件反射与说话人分离的负距离. IEEE机器学习与newbb电子国际会议(ICMLA'22).
  • Song, S., Saunders, K., Yue, Y., Liu, J. (2022). 基于深度强化学习的平滑轨迹碰撞避免. IEEE机器学习与newbb电子国际会议(ICMLA'22).
  • Sun, T., Gong, S., Wang, Z., Smith, C., Wang, X., Xu, L., Liu, J. (2021). 通过自监督表示提高波形语音增强网络的可理解性. IEEE机器学习与newbb电子国际会议(ICMLA'21).
  • Smith, C., McGee, G., Gogineni, S., Bergin, J., Liu, J. (2021). 雷达中脉冲神经网络的评价. 2021 IEEE National Aerospace & Electronics Conference.
  • McGee, G., Smith, C., Gogineni, S., Bergin, J., Liu, J. (2021). 雷达辐射源探测的网络融合. 2021 IEEE National Aerospace & Electronics Conference.
  • Abuhajar, N., Sun, T., Wang, Z., Gong, S., Smith, C., Wang, X., Xu, L., Liu, J. (2021). 高效鲁棒语音增强的网络压缩和帧拼接. 2021 IEEE National Aerospace & Electronics Conference.
  • Song, S., Zhang, Y., Qin, X., Saunders, K., Liu, J. (2021). 基于深度强化学习的视觉避碰. 雅典:2021年IEEE国家航空航天 & Electronics Conference.
  • Gong, S., Wang, Z., Sun, T., Smith, C., Xu, L., Liu, J. (2019). 扩张型FCN:听得越久越好. IEEE信号处理在音频和声学中的newbb电子研讨会(WASPAA'19); http://www.waspaa.com/.
  • Wang, Z., Cai, W., Rudmann, D., Liu, J., Rosol, T. (2019). 基于QuPath脚本的甲状腺胶质细胞和滤泡细胞自动分割. 第38届环境毒理学病理学与同一健康年度研讨会; http://www.toxpath.org/AM2019/.
  • Wang, Z., Cai, W., Smith, C., Kantake, N., Rosol, T., Liu, J. (2019). 残差金字塔FCN稳健滤泡分割. 2019年IEEE生物医学成像国际研讨会(ISBI 2019); http://biomedicalimaging.org/2019/.
  • Wang, Z., Cai, W., Kantake, N., Liu, J., Rosol, T. (2018). 神经网络和深度学习开发甲状腺肥大自动图像分析算法. 2018 ACVP Annual Meeting; http://www.acvp.org/page/2018Meeting.
  • Wang, Z., Cai, W., Smith, C., Kantake, N., Rosol, T., Liu, J. (2018). 残差金字塔FCN稳健滤泡分割. 2019年IEEE生物医学成像国际研讨会(ISBI 2019); http://biomedicalimaging.org/2019/.
  • Wang, Z., Smith, C., Liu, J. (2018). 多尺度fnc集成改进白质病灶分割. 2018年医学成像机器学习国际会议; http://link.springer.com/chapter/10.1007/978-3-030-00919-9_26.
  • Wang, Z., Shi, B., Smith, C., Liu, J. (2018). 李群内测地线插值非线性度量学习. 模式识别国际会议(ICPR'2018); http://ieeexplore.ieee.org/document/8545287.
  • Chen, Y., Shi, B., Zhang, P., Smith, C., Liu, J. (2018). 基于深度网络的多模态特征融合用于AD/MCI诊断. 2018 IEEE生物医学成像国际研讨会(ISBI'2018).
  • Chen, Y., Wang, Z., Smith, C., Liu, J. (2018). 基于序列FCN的三维脑肿瘤分割. 2018 IEEE生物医学成像国际研讨会(ISBI'2018).
  • Liao, B., Chen, Y., Wang, Z., Smith, C., Liu, J. (2018). Whole-brain 1.通过多视图FCN进行5 - 3T MRI转换. 模式识别国际会议(ICPR’2018).
  • Chen, Y., Shi, B., Wang, Z., Sun, T., Smith, C., Liu, J. (2017). 基于卷积LSTM和视图集成的海马准确一致分割. 医学影像中的机器学习; http://link.springer.com/chapter/10.1007/978-3-319-67389-9_11.
  • Zhang, P., Shi, B., Smith, C., Liu, J. (2017). 非线性特征空间变换改进MCI到AD转换的预测. 医学图像计算与计算机辅助干预会议(MICCAI' 2017); http://www.springer.com/cda/content/document/cda_downloaddocument/9783319661780-c2.pdf?SGWID=0-0-45-1615912-p181094091.
  • Chen, Y., Shi, B., Zhang, P., Smith, C., Wang, Z., Liu, J. (2017). 基于多视图集成卷积神经网络的海马体分割. 2017 IEEE生物医学成像国际研讨会(ISBI 2017); http://ieeexplore.ieee.org/document/7950499/.
  • Zhang, P., Shi, B., Smith, C., Liu, J. (2016). 基于相干点漂移的半监督学习非线性度量学习 . IEEE机器学习与newbb电子国际会议(ICMLA'2016); http://ieeexplore.ieee.org/document/7838162/.
  • Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. (2016). 基于四网格的阿尔茨海默病径向距离生物标志物. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI'2016); 19-23. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7493201.
  • Chen, Y., Shi, B., Smith, C., Liu, J. (2015). 非线性特征变换与深度融合用于阿尔茨海默病分期分析. LNCS . Switzerland: Machine Learning in Medical Imaging (MLMI'2015); 9352: pp. 304-312. http:://link.springer.com/chapter/10.1007%2F978-3-319-24888-2_37.
  • Shi, B., Chen, Y., Hobbs, K., Smith, C., Liu, J. (2015). 综合纵向神经影像学特征的非线性度量学习在阿尔茨海默病诊断中的newbb电子. 1-901725-53-7. 英国机器视觉会议(BMVC'2015); http://www.bmva.org/bmvc/2015/papers/paper138/index.html.
  • Shi, B., Wang, Z., Liu, J. (2014). 阿尔茨海默病分期的距离通知度量学习. 2014年IEEE医学与生物工程学会国际年会(EMBC'2014); http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6943745.
  • Hobbs, K., Zhang, P., Liu, J. (2014). 固有径向距离对阿尔茨海默病海马萎缩的鲁棒估计. 2014年IEEE医学与生物工程学会国际年会(EMBC'2014).
  • Shi, B., Liu, J., Berryman, D., List, E., Kelder, B., Kopchick, J. (2013). 用于肥胖研究的小鼠全身统计形状图谱的开发. 2013 BMES Annual Meeting.
  • Xu, H., Zhang, P., Liu, J. (2013). 基于光谱形状分析框架的阿尔茨海默病(AD)形状生物标志物鉴定. 2013 BMES Annual Meeting; http://bmes.org/meetings.
  • Shi, B., Liu, J., Xie, S., Berryman, D., List, E. (2013). 小鼠显微ct对内脏和皮下脂肪组织的稳健分离. 第35届IEEE医学与生物工程学会国际年会(EMBC'2013); http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6610000.
  • Xu, H., Liu, J. (2013). 鲁棒形状匹配的空间感知谱嵌入. 国际声学，语音和信号处理会议(ICASSP'2013); http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6638019.
  • Xie, S., Liu, J., Smith, C. (2012). 基于子午曲线的黎曼形状分析. 1. IEEE 11th International Conference on Machine Learning and Applications (ICMLA'2012); 1: 532-537. http://ieeexplore.ieee.org/document/6406618/.
  • Xie, S., Liu, J., Smith, C. (2012). 基于曲线骨架的形状表示与分类. 国际图像处理会议(ICIP) 2012; http://icip2012.com/Papers/PublicSessionIndex3.asp?Sessionid=1204.
  • Xu, H., Liu, J., Smith, C. (2012). 基于聚类和广义径向基函数(C-GRBF)的鲁棒高效点配准. 国际图像处理会议(ICIP'2012); http://ieeexplore.ieee.org/document/6467198/.
  • Zhang, W., Liu, J., Liu, Z. (2012). 无线移动网络与计算的自适应重传方案. 2012 International Conference on Systems and Informatics (ICSAI'2012); 56 - 62 . http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6223067.
  • Zhang, W., Liu, J., Liu, Z. (2012). 无线移动网络与计算的自适应重传方案.. 青岛:系统与信息学(ICSAI)， 2012国际学术会议; http://ieeexplore.ieee.org/document/6223067/.
  • Xie, S., Liu, J., Smith, C. (2012). 基于曲线骨架的形状分析新框架. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'2012); 701-704. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6268628.
  • Shi, B., Liu, J. (2012). 医学图像配准中的正则性保证变换估计. Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141W; 8314: http://proceedings.spiedigitallibrary.org/mobile/proceeding.aspx?articleid=1345935.
  • Shi, B., Liu, J. (2011). 变形估计的非扭转正则化. London: Medical Image Analysis and Understanding (MIAU'2011); 151-156. http://www.bmva.org/_media/miua:miua2011proceedings.pdf.
  • Mourning, C., Nykl, S., Xu, H., Chelberg, D., Liu, J. (2010). 鲁棒点匹配的GPU加速. 417--426.
  • Nykl, S., Mourning, C., xu, H., Chelberg, D., Liu, J. (2010). 计算机科学课程讲稿(6455, Advances in Visual Computing, 章节标题:稳健点匹配的GPU加速. Advances in Visual Computing. Berlin Heidelberg: Springer-Verlag; 6455: 417-426. http://www.springerlink.com/content/c2g8n75p68njq875/.
  • Liu, J., Smith, C., Chebrolu, H. (2009). 基于积分平方估计的多发性硬化症自动检测. 计算机视觉及模式识别工作坊(CVPRW 2009); http://ieeexplore.ieee.org/iel5/5191364/5204041/05204351.pdf.
  • Xie, S., Liu, J., Berryman, D., List, E., Smith, C., Chebrolu, H. (2007). 基于积分平方估计的鲁棒图像分割模型. 国际视觉计算研讨会(ISVC'2007); http://link.springer.com/chapter/10.1007/978-3-540-76856-2_63.
  • Liu, J., Smith, C., Chebrolu, H. (2007). 基于概率图谱的皮质下结构自动分割. 国际视觉计算研讨会(ISVC'2007); http://link.springer.com/chapter/10.1007/978-3-540-76858-6_17.
  • Liu, J., Smith, C., Chebrolu, H. (2007). 基于局部似然的活动轮廓自动皮层下结构分割. 3D Segmentation in The Clinic: A Grand Challenge 2007; pp. 91-98. http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p91.pdf.
  • Liu, J., Chelberg, D., Chebrolu, H., Smith, C. (2007). 基于分布的脑MR图像水平集分割. 英国机器视觉会议论文集; http://www.bmva.org/bmvc/2007/papers/paper-222.html.
  • Liu, J. (2007). 基于局部概率先验的脑磁共振图像分割活动轮廓模型. Asian Conference on Computer Vision (ACCV'2007); pp 956-964. http://link.springer.com/chapter/10.1007/978-3-540-76856-2_63.
  • Liu, J., Wang, Y., Liu, J. (2006). 分割辅助图像配准的统一框架. Asian Conference on Computer Vision (ACCV 2006); pp 405-414. http://link.springer.com/chapter/10.1007/11612704_41.
  • Liu, J. (2006). 基于局部中值的鲁棒图像分割. Computer and Robot Vision, 2006. The 3rd Canadian Conference on; http://ieeexplore.ieee.org/document/1640386/.
  • Li, C., Liu, J., Fox, M. (2005). 保持边缘的梯度向量流分割:蛇形的自动初始化和分裂方法. 计算机视觉与模式识别(CVPR'2005); http://ieeexplore.ieee.org/document/1467263/.
  • Liu, J. (2005). 医学图像分割导向配准. SPIE Medical Imaging; http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1282082.
  • Wang, Y., Liu, J. (2005). 基于局部相关的分割引导鲁棒多模态图像配准. IEEE ESBS年度国际会议(ESBC'05); http://www.ncbi.nlm.nih.gov/pubmed/17282886.
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  • Sun, T., Wang, Z., Wang, Z., Smith, C., Liu, J. (2020). TraceCaps:一种基于胶囊的语义分割神经网络. ArXiv:计算机视觉与模式识别; http://arxiv.org/pdf/1901.02920.pdf.

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