吴国庆 -亚博vip888

• 医学信号处理（深度学习，医疗影像大数据，影像组学，医学影像处理）
• 人工智能辅助诊断（基于磁共振、超声、ct的人工智能辅助诊断系统）
• 医学超声成像（成像原理、方法，非线性超声成像）

主持科研项目情况：

• 国家自然科学基金青年项目

• 中国博士后科学基金面上项目

• 国家自然科学基金重大研究计划

• 复旦大学人才引进项目

• 2022年 复旦大学优秀博士论文

• 2019年 飞利浦医疗产品创意大赛铜奖

• 2019年 上海市超级博士后

• 2019年 复旦大学优秀毕业生

• 2018年 博士国家奖学金

• 2017年 中国生物医学工程会议优秀论文奖

• 202203- ：复旦大学生物医学工程中心 青年副研究员

• 201907-202202：复旦大学电子科学与技术博士后流动站 博士后

• 201906-201906：复旦大学生物医学工程专业 博士学位

• 201309-201606： 重庆大学信号与信息处理专业 硕士学位

研究生课程：现代信息与信号处理理论

1. wu guoqing, chen xi, shi zhifeng，et al.,convolutional neural network with coarse-to-fine resolution fusion and residual learning structures for cross-modality image synthesis. biomedical signal processing and control,2022,volume 71, part b.

2. wu guoqing#, jiang zhaoshun#, cai yuxi, et al. multi-order brain functional connectivity network-based machine learning method for recognition of delayed neurocognitive recovery in older adults undergoing non-cardiac surgery. frontiers in neuroscience,2021:1-13.

3. xiong siyu#, wu guoqing#, fan xitian, et al. mri‑based brain tumor segmentation using fpga‑accelerated neural network. bmc bioinformatics,2021:1-15.

4.  wu guoqing#, chen xi#, lin jixian# , et al. identification of invisible ischemic stroke in noncontrast ct based on novel two-stage convolutional neural network model. medical physics, 2021, 48.

5. pan jiawei, wu guoqing, yu jinhua , et al. detecting the early infarct core on non-contrast ct images with a deep learning residual network. journal of stroke and cerebrovascular diseases, 2021, 30(6):105752.

6. wu guoqing, shi zhifeng, chen yinsheng , et al. a sparse representation-based radiomics for outcome prediction of higher grade gliomas. medical physics, 2018, 46.

7. wu guoqing#, lin jixian#, wang yuanyuan* , et al. early identification of ischemic stroke in noncontrast computed tomography. biomedical signal processing and control, 2019, 52(jul.):41-52.

8. wu guoqing, chen yinsheng, wang yuanyuan*, , et al. sparse representation-based radiomics for the diagnosis of brain tumors. ieee transactions on medical imaging, 2017.

9. 吴国庆, 李泽榉, 汪源源,等. 基于稀疏表示体系的原发性脑部淋巴瘤和胶质母细胞瘤图像鉴别. 生物医学工程学杂志, 2018, 35(5):7.

10. guoqingwu, yuanyuanwang, jinhuayu. 3d texture feature learning for noninvasive estimation of gliomas pathological subtype/ international miccai brainlesion workshop. springer, cham, 2018.

11. wu guoqing, wang yuanyuan*, yu jinhua* . overall survival time prediction for high grade gliomas based on sparse representation framework. springer, cham, 2017.

12. liu shujun*, wu guoqing, liu hongqing , et al. image restoration approach using a joint sparse representation in 3d-transform domain. digital signal processing, 2017, 60:307-323.

13. liu shujun*, wu guoqing, zhang xinzheng, et al. sar despeckling via classification-based nonlocal and local sparse representation. neurocomputing, 2016, 219(jan.5):174-185.