姓名：周 武
职称/职务：研究员、杏林青年学者
电话：02039358157
邮箱：zhouwu@gzucm.edu.cn
教育背景:
工学学士 武汉工程大学 2001.9 - 2005.6
工学硕士 广东工业大学 2005.9 - 2008.6
工学博士 华南理工大学 2008.9 - 2012.6
联合培养博士生 莱特州立大学（美国） 2010.9 - 2012.3
工作经历:
博士后 中国科学院计算技木所, 2012.7 - 2014.6
助理研究员 中国科学院深圳先进技木研究院,2014.7 - 2017.9
研究员 广州中医药大学, 2017.10 - 至今
研究领域:
临床肿瘤影像、医学影像分析、中医望诊智能诊断、深度学习和人工智能
研究概况:
临床肿瘤影像：基于影像组学和深度学习技术开展临床肿瘤的生物学特性分析研究
医学影像分析：基于深度学习技术开展医学影像的分类、分割、配准等临床应用研究
中医望诊智能诊断：基于中医诊断学理论和计算机视觉技术开展舌诊和面诊智能诊断研究
深度学习和人工智能：开展深度特征表征、多模态融合、超分辨率重建等方法学研究
研究课题:
国家自然科学基金面上基金： 多模态深度特征融合表征肝细胞癌生物侵袭性研究
(No.81771920，55万) 主持 (2018.1-2021.12)
国家自然科学基金青年基金： 大尺寸高分辨率差异图像的结构化分层细分配准研究
(No.61302171，24万) 主持 (2014.1-2016.12)
中国博士后科学基金：基于拓扑几何结构自适应径向基函数的医学图像配准
(No.2013M530740，5万) 主持 (2013.5-2014.10)
深圳市基础研究项目：基于多特征融合和图结构自适应径向基函数的医学影像配
准(No.JCYJ20150630114942291，30万) 主持 (2016.1-2017.12)
国家自然科学基金-广东省联合重点项目：磁共振热成像引导射频消融木精准治疗
肝癌的关键问题研究(No.U1301258，270万) 子课题负责人, (2014.1-2017.12)
国家留学基金委公派留学项目： 精密图像配准方法研究(No:10561070)
受资助对象 (2010.9-2012.3)。
注：以上内容按“个人介绍样板”制作，以下内容是科研情况，视学院网站选择性刊发*****************************************************************************
学生培养：
[1]王齐耀 2016.7-2017.8 发表成果ICIP2017
[2]窦天佑 2017.10-2018.6 发表成果ISBI2018(oral), ICPR2018, MICCAI2018
[3]鞠涵秋2018.6-2019.6 发表成果ISBI2019, ICIP2019, MICCAI2019
[4]简婉薇2018.9-2020.6 发表成果EMBC2019, ISMRM2020(oral)
[5]李酝灵2019.7-2020.6 发表成果ISBI2020, ISMRM2020(oral power-pitch)
[6] 黄荟 2018.9-2020.6 发表成果ISMRM2020(oral)
期刊论文成果:
[1] Wu Zhou*, Guangyi Wang, Guoxi Xie, Lijuan Zhang. Grading of hepatocellular carcinoma based on diffusion weighted images with multiple b-values using convolutional neural networks. Medical Physics 2019;46(9): 3951-3960.
[2] Wu Zhou, Lijuan Zhang, Kaixin Wang, Shuting Chen, Guangyi Wang, Zaiyi Liu, Changhong Liang. Malignancy Characterization of Hepatocellular Carcinomas Based on Texture Analysis of Contrast-Enhanced MR Images Journal of Magnetic Resonance Imaging 2017 ;45(5):1476-1484.
[3] Wu Zhou, Arthur Goshtasby, Adaptive image registration via hierarchical Voronoi subdivision. IEEE Transactions on Image Processing 2012;21(5):2464-2473.
[4] Wu Zhou, Yaoqin Xie. Interactive Multigrid refinement for deformable image registration. Biomedical Research International. Volume 2013, Article ID 532936, 9 pages.
[5] Wu Zhou, Yaoqin Xie, Lijuan Zhang and Changhong Liang. A novel Technique for Pre-alignment in Multimodality medical image registration. Biomedical Research International. Volume 2014, Article ID 726852, 16 pages.
[6] Wu Zhou, Yaoqin Xie. Interactive contour delineation and refinement in treatment planning of image guided radiation therapy. Journal of Applied Clinical Medical Physics. Vol.15,No.1, 2014:4499.
[7] Wu Zhou, Yaoqin Xie. Interactive medical image segmentation using Snake and Multiscale Curve Editing. Computational and Mathematical Methods in Medicine. Volume 2013, Article ID325903, 13 pages.
[8]周武，谢耀钦，田洋洋. 影像引导放疗中交互式肿瘤靶区勾画方法,《集成技术》，2014(1)：68-76.
[9]周武,胡跃明,基于相位相关和图像重采样的亚像素级复合配准定位算法，《华南理工大学学报》,2010,38(10)：68-73, 2010.
[10]周武,胡跃明,刘屿. 基于投影和重采样技木的目标旋转角度则量方法，《光学技术》，Vol. 36, No.6, pp.860-865, 2010.
国际会议论文：
[1]Wu Zhou, Kaixin Wang, Lijuan Zhang, Shuting Chen, Guangyi Wang, Zaiyi Liu, and Changhong Liang. Texture analysis of hepatocellular carcinomas in Contrast-enhanced MR images for malignant differentiation. Proc. Intl. Soc. Mag. Reson. Med. 24:1125(2016, Oral presentation).
[2]Wu Zhou, Qiyao Wang, Su Yao, Guangyi Wang, Zaiyi Liu, Changhong Liang, Lijuan Zhang. Quantitative texture feature to predict Microscopic portal vein invasion of Hepatocellular carcinoma with contrast-enhanced MR images. Proc. 25th Annual Meeting of ISMRM, Honolulu, USA, 2017, ID.5179（Oral Power Pitch presentation）
[3]Wu Zhou, Qiyao Wang, Guangyi Wang, Zaiyi Liu, Changhong Liang, Lijuan Zhang. Differentiation of low- and high- grade hepatocellular carcinomas with texture features and a machine learning model in arterial phase of contrast-enhanced MR. Proc. 25th Annual Meeting of ISMRM, Honolulu, USA, 2017, ID.3997（E-Poster presentation）
[4]Wu Zhou, Qiyao Wang, Hairong Zheng, Lijuan Zhang. Quantitative analysis of Diffusion-weighted MRI and Contrast-enhanced MRI for estimating histopathological grade of hepatocellular carcinomas. The RSNA 103rd Scientific Assembly and Annual Meeting, November 26 - December 1, 2017, Chicago, Illinois. ID 17006207. (Oral Presentation)
[5]Wu Zhou, Qiyao Wang, Hairong Zheng, Lijuan Zhang. Using deep learning to investigate the value of ’washin and washout’ in hepatocellular carcinoma for malignancy characterization. The RSNA 103rd Scientific Assembly and Annual Meeting, November 26 - December 1, 2017, Chicago, Illinois. ID 17006331. (Oral Presentation)
[6]Wu Zhou, Qiyao Wang, Hairong Zheng, Lijuan Zhang. Characterization of malignancy of hepatocellular carcinoma using deep feature with contrast-enhanced MR. The RSNA 103rd Scientific Assembly and Annual Meeting, November 26 - December 1, 2017, Chicago, Illinois. ID 17006248. (Oral Presentation)
[7]Qiyao Wang, Lijuan Zhang, Yaoqin Xie, Hairong Zheng, andWu Zhou*. Malignancy characterization of hepatocellular carcinoma using hybrid texture and deep feature. Proc.24th IEEE Int. Conf. Image Process, 2017:4162-4166.
[8]Tianyou Dou, Lijuan Zhang, Wu Zhou*. 3D Deep feature fusion in Contrast-enhanced MR for malignancy characterization of hepatocellular carcinoma. International Symposium on Biomedical Imaging Conference of the IEEE Engineering in Medicine and Biology Society, ISBI'18：29-33 (Oral Presentation)
[9]Wu Zhou, Qiyao Wang, Guoxi Xie, Fei Yan, Guangyi Wang, Zaiyi Liu, Changhong Liang, Hairong Zheng, Lijuan Zhang. Motion Correction of Diffusion-weighted imaging in the analysis of Apparent Diffusion Coefficient for preoperative staging of hepatocellular carcinoma. Proc. 26th Annual Meeting of ISMRM, ID:4631, Paris, France 16-21 June 2018. (Oral Presentation)
[10]Wu Zhou, Qiyao Wang, Changhong Liang, Hairong Zheng, Lijuan Zhang. Using deep learning to investigate the value of diffusion weighted images for malignancy characterization of hepatocellular carcinoma. Proc. 26th Annual Meeting of ISMRM, ID:4690, Paris, France 16-21 June 2018. (E-Poster Presentation)
[11]Wu Zhou, Tianyou Dou, Miaoyun Zhangwen, Hui Ye, Dong Cao, Honglai Zhang, Guangyi Wang, Hairong Zheng, Lijuan Zhang. Discriminative deep feature fusion of Contrast-enhanced MR for malignancy characterization of hepatocellular carcinoma. Proc. 26th Annual Meeting of ISMRM, ID:5068, Paris, France 16-21 June 2018.(E-Poster Presentation)
[12]Tianyou Dou, Wu Zhou*. 2D and 3D Convolutional Neural Network fusion for predicting the histological grade of hepatocellular carcinoma. IAPR International Conference on Pattern Recognition (ICPR’2018), pp.3832-3837, 2018.
[13]Tianyou Dou, Lijuan Zhang, Hairong Zheng, Wu Zhou*. Local and nonlocal deep feature fusion for malignancy characterization of hepatocellular carcinoma. International Conference on Medical Image Computing and Computer Assisted Intervention(MICCAI), LNCS 11073, pp. 472-479, 2018.
[14]Shaoyang Men, Hanjiu Ju, Lijuan Zhang, Wu Zhou*. Prediction of Microvascular invasion of hepatocellular carcinoma with Contrast-enhanced MR using 3D CNN and LSTM. International Symposium on Biomedical Imaging Conference of the IEEE Engineering in Medicine and Biology Society, 2019 :810-813.
[15]Wu Zhou, Hanqiu Ju, Wanwei Jian, Shaoyang Men, Honglai Zhang. Can preoperative Diffusion-weighted MR predict the Microvascular invasion of hepatocellular carcinoma? A deep learning evaluation. Proc. 27th Annual Meeting of ISMRM, ID:6628, Monteria, Canada 11-16 May 2019. (Oral Power pitch Presentation)
[16]Wu Zhou, Yaoqin Xie, Guangyi Wang. 3D Convolutional Neural Network with Contrast-enhanced MR for Microvascular invasion prediction of hepatocellular carcinoma. Proc. 27th Annual Meeting of ISMRM, ID:6628, Monteria, Canada 11-16 May 2019. (Oral Presentation)
[17]Wanwei Jian, Hanqiu Ju, Xiaoping Cen, Manman Cui, Honglai Zhang, Lijuan Zhang, Guangyi Wang, Lin Gu, Wu Zhou*. Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR. 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, July 23-27, 2019: 853-856.
[18]Hanqiu Ju, Guangyi Wang, Shaoyang Men, Wu Zhou*. Discrepancy steered Conditional Adversarial network for deep feature based malignancy characterization of hepatocellular carcinoma. 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, September 21-25, 2019:1342-1345.
[19]Hanqiu Ju, Wanwei Jian, Xiaoping Cen, Guangyi Wang, Wu Zhou*. Similarity steered generative adversarial network and adaptive transfer learning for malignancy characterization of hepatocellualr carcinoma. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), October 13-17, Shenzhen, China, pp. 567-574, 2019.
[20]Yunling Li, Hui Huang, Lijuan Zhang, Guangyi Wang, Honglai Zhang, Wu Zhou*. Super resolution and self-attention with generative adversarial network for improving malignancy characterization of hepatocellular carcinoma. International Symposium on Biomedical Imaging Conference of the IEEE Engineering in Medicine and Biology Society, April 4-7, Iowa, US, pp. 1556-1560, 2020.
[21]Wu Zhou, Yunling Li, Hui Huang, Yaoqing Xie, Lijuan Zhang, and Guangyi Wang. Super-resolution Generative Adversarial Network for improving malignancy characterization of hepatocellular carcinoma. Proc. Intl. Soc. Mag. Reson. Med (ISMRM) 2020 ID:4593. (Accepted, Oral Power-pitch presentation)
[22]Wu Zhou, Hui Huang, Guangyi Wang, Honglai Zhang. Local feature denoising and global feature extraction for malignancy characterization of hepatocellular carcinoma. Proc. Intl. Soc. Mag. Reson. Med (ISMRM) 2020 ID:4534. (Accepted, Oral presentation)
[23]Wu Zhou, Wanwei Jian, Guangyi Wang. Improving the performance of non-enhanced MR for predicting the grade of hepatocellular carcinoma by transfer learning. Proc. Intl. Soc. Mag. Reson. Med (ISMRM) 2020 ID:4651. (Accepted, Oral presentation)
专利成果：
[1]一种精密电子组装中微位移检测方法. 发明专利授权号：ZL 201010214839.7
[2]基于梯度直方图分布匹配确定三维旋转量的方法和系统.发明专利授权号： ZL201410708512.3
[3] 一种基于影像信息融合的病灶虚拟穿刺系统.发明专利授权号：ZL201510896226.9
[4]一种外科手木中CT影像体表人工标记自动提取方法.发明专利授权号：ZL201610448693.X
[5]一种X光影像中人工圆形标记的自动检则和定位方法.发明专利授权号：ZL201610442687.3
[6]一种CT影像体表提取方法及系统. 发明专利授权号：ZL201610444521.5
软件成果：医学影像软件著作权登记号2015SR229598
科研合作：
广东省中医院影像科、 中国科学院深圳先进技术研究院
广东省人民医院放射科、 拜耳医药(中国)影像诊断部
南方医院影像科、 日本东京大学/RIKEN,AIP
江苏省人民医院影像科、
