姓名：吴兴隆
出生年月：1979年8月
学历（学位）：博士
毕业院校：浙江大学（本科）
美国University of Miami（硕士/博士）
现任职务：古天乐太阳娱乐集团tyc493计算机学院副教授，硕士生导师
主要社会兼职：湖北省青年科协常务理事，湖北省计算机学会理事
表彰及荣誉：湖北省组织部第四批人才计划特聘专家（2015年）
湖北省教育厅“人才计划”（2018年）
江苏省组织部“双创计划”创新人才（2019年）
个人经历：
2008.01-2011.04, 美国University of Miami, 博士后/Assistant Scientist
2010.06-2010.12，美国芝加哥大学Computation Institute, 访问学者
主要研究方向：
（1）生物医学图像处理
（2）机器视觉
（3）嵌入式开发和应用
科研项目：近年来共主持和参加国家级研究中心开放课题、湖北省自然科学基金面上项目、省教育厅科学研究计划指导项目、国家人社部和湖北省人社厅留学回国人员择优项目研究，以及一批企业委托横向科研项目的研究工作。
主持和参与的部分项目：
[1] “Docker模式深度学习技术在全脑三维重建中应用研究”，武汉光电国家实验室开放课题，主持；
[2] “自增长式的白名单人脸识别和身份确认云平台”, 湖北省教育厅科学研究计划指导项目, 主持；
[3] “人工智能技术对肾脏肿块性疾病的二维超声诊断研究”,参与湖北省自然科学基金面上项目，参与
[4] “私有云平台中的智慧云计算集群”，人社部留学回国人员科技活动择优资助项目优秀类，主持；
[5] “企业级私有云平台”, 人社部留学回国人员科技活动择优资助项目启动类，主持；
科研成果：在国内外重要学术刊物上发表SCI科研论文近20篇。申请发明专利近10项，软件著作权10余项。部分成果如下：
[1] X. Cao, Y. Fang, C. Yang, Z. Liu, G. Xu, Y. Jiang, P. Wu, W. Song, H. Xing, X. Wu*, "Prediction of Prostate Cancer Risk Stratifications Based on A Non-Linear Transformation Stacking Learning Strategy," International neurourology Journal, 2024;28(1):33-43. https://doi.org/10.5213/inj.2346332.166
[2] X. Cao, X. Wu*, et al., "PFCA-Net: a post-fusion based cross-attention model for predicting PCa Gleason Group using multiparametric MRI," 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 3507-3513. https://doi.org/10.1109/BIBM58861.2023.10385606
[3] G. Xu, X. Leng, C. Li, X. He and X. Wu*, "MGFuseSeg: Attention-Guided Multi-Granularity Fusion for Medical Image Segmentation," 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 3587-3594, https://doi.org/10.1109/BIBM58861.2023.10385461.
[4] Xu, G., Zhang, X., He, X., X. Wu* (2024). LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14432. Springer, Singapore. https://doi.org/10.1007/978-981-99-8543-2_4
[5] W., Wu. L., Jiang, Y., Xing, H., Song, P., Cui, X. Wu, X. L.*, & Xu, G.* (2023). Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study. Physics in medicine and biology. https://doi.org/10.1088/1361-6560/acec2d
[6] Wu, X. L., Jiang, Y., Xing, H., Song, W., Wu, P., Cui, X. W., & Xu, G. (2023). ULS4US: universal lesion segmentation framework for 2D ultrasound images. Physics in medicine and biology. https://doi.org/10.1088/1361-6560/ace09b.
[7] Guoping Xu, Wentao Liao, Xuan Zhang, Chang Li, Xinwei He, and Xinglong Wu. 2023. Haar wavelet downsampling: A simple but effective downsampling module for semantic segmentation. Pattern Recogn. 143, C (Nov 2023). https://doi.org/10.1016/j.patcog.2023.109819
[8] Yang, C., Liu, Z., Fang, Y., Cao, X., Xu, G., Wang, Z., Hu, Z., Wang, S., & Wu, X. (2023). Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte. Journal of translational medicine, 21(1), 465. https://doi.org/10.1186/s12967-023-04318-w
[9] Rong Xiao, Lei Zhu, Jiangshan Liao, Xinglong Wu, Hui Gong, Jin Huang, Ping Li, Bin Sheng, Shangbin Chen,,DDeep3M+: adaptive enhancement powered weakly supervised learning for neuron segmentation, Neurophoton. 10(3), 035003 (2023), https://doi.org/10.1117/1.NPh.10.3.035003.
[10] W. Liao, G. Xu*, X. Wu*, X. Zhang and C. Li, "Dual-branch body and boundary supervision network for ultrasound image segmentation," 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022, pp. 3071-3077, https://doi.org/10.1109/BIBM55620.2022.9995339.
[11] G. Xu, X. Wu*, X. Zhang, W. Liao and S. Chen (2022). LGNet: Local and Global Representation Learning for Fast BioMedical Image Segmentation. Journal of Innovative Optical Health Sciences, https://doi.org/10.1142/S1793545822430015.
[12] Wu, X., Li, M., Cui, X. W., & Xu, G. (2022). Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer. Physics in medicine and biology, 67(3),https://doi.org/10.1088/1361-6560/ac4c47.
[13] G. Xu and X. Wu*, "FAM: Fully Attention Module for Medical Image Segmentation," 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021,https://doi.org/10.1109/BIBM52615.2021.9669567.
[14] Wu X, Tao Y, He G, Liu D, Fan M, Yang S, Gong H, Xiao R, Chen S and Huang J., 2021: Boosting Multilabel Semantic Segmentation for Somata and Vessels in Mouse Brain. Front. Neurosci. https://doi.org/10.3389/fnins.2021.610122
[15] Huang, J., He, R., Chen, J, Wu. X* (2021). Boosting Advanced Nasopharyngeal Carcinoma Stage Prediction Using a Two-Stage Classification Framework Based on Deep Learning. Int J Comput Intell Syst 14, 184.https://doi.org/10.1007/s44196-021-00026-9.
[16] X.L. Wu, S.B. Chen*, J. H.*, A. Li, R. Xiao, X.W. Cui, 2020: DDeep3M: Docker-powered deep learning for biomedical image segmentation. Journal of Neuroscience Methods.https://doi.org/10.1016/j.jneumeth.2020.108804
[17] Zhou L., Wu, X., Cui*. X., etc. 2019: Lymph node metastasis prediction from primary breast cancer ultrasound images using deep learning. Radiology.https://doi.org/10.1148/radiol.2019190372.
[18] X. Kong, S. Yan, E. Zhou, J. Huang, X. Wu, P. Wang, and S. Chen, "DDeep3M-based neuronal cell counting in 2D large-scale images," in Optics in Health Care and Biomedical Optics IX (SPIE, 2019), pp. 1119037
[19] Zhou L., Wang J., Yu S., Wu G., Wei Q., Deng Y., Wu X., Cui* X. and Dietrich C., 2019: Artificial intelligence in medical imaging of liver. World Journal of Gastroenterology.https://doi.org/10.3748/wjg.v25.i6.672
部分授权和公示中的发明专利
[1] 一种基于云计算的云集群快速部署系统，专利号 201410375022.6
[2] 一种新型NOR Flash译码电路，授权号CN104464808A
[3] 基于多模态深度学习分类模型的模态贡献度的计算方法, CN202110563034.1
[4] 基于深度卷积神经网络的提升式多标签语义分割方法, CN202110448155.1
[5] 预测前列腺癌风险分层的临床特征-机器学习列线图方法, CN202310687169.8
[6] 基于多模态深度学习影像组学的乳腺癌超声图像, CN202310688889.6
[7] 基于编解码结构的经颅磁刺激电场快速成像方法, CN202210055122.5
教学工作：先后承担5门本科生和1门研究生课程的讲授任务。目前主要讲授的课程有：“Python程序设计”、“机器学习”、“Python数据分析与挖掘”、“数字图像处理”、“操作系统”等。
联系方式：xwu@wit.edu.cn
欢迎有志于在生物医学图像处理、机器视觉算法开发和基于FPGA的人工智能算法加速等相关领域的同学报考！
