傅宇

学历：博士研究生

生日：1994年1月6日

籍贯：山东省潍坊市

邮箱：fuyu@ouc.edu.cn

研究方向：医学人工智能

单位：中国海洋大学信息科学与工程学部

地址：山东省青岛市黄岛区三沙路1299号

博士毕业于山东大学，师从董恩清教授，目前为中国海洋大学信息科学与工程学部讲师。主要从事深度学习、医学影像处理等方面的科研工作，主攻方向为面向小样本及不均衡问题的医学影像分类方法研究。谷歌学术网址: https://scholar.google.com/citations?user=OkwY0n4AAAAJ&hl=en

教育经历

最新消息

  1. 针对肺癌亚型诊断的论文被国际期刊 BSPC 接受！ (2024年6月25日)

发表论文

  1. Yu Fu#, Changli Liu, Shaoqiang Wang, Enqing Dong,Hui Xia, “SSAL-Net: Semi-supervised Network integrating Self-supervised Adversarial Learning for Diagnosing Subtypes of Pulmonary Nodules,” Biomedical Signal Processing and Control, Jun 2024. (SCI: IF=5.076, JCR Q1)
  2. Yu Fu#, Peng Xue, Zhili Zhang, Enqing Dong*, “PKA2-Net: Prior Knowledge-based Attention Network for Accurate Pneumonia Diagnosis on Chest X-ray Image,” IEEE Journal of Biomedical and Health Informatics, April 2023. (SCI: IF=7.021, JCR Q1)
  3. Yu Fu#, Hong Zhang, Peng Xue, Meirong Ren, Taohui Xiao, Zhili Zhang, Yong Huang, Enqing Dong*, “Qualitative Analysis of PD-L1 Expression in Non-Small-Cell Lung Cancer Based on Chest CT Radiomics,” Biomedical Signal Processing and Control, Online, March 2023. (SCI: IF=5.076, JCR Q1)
  4. Yu Fu#, Peng Xue, Taohui Xiao, Zhili Zhang, Youren Zhang, Enqing Dong*, “Semi-Supervised Adversarial Learning for Improving the Diagnosis of Pulmonary Nodules,” IEEE Journal of Biomedical and Health Informatics, vol.27(1):109-120,2023. (SCI: IF=7.021, JCR Q1) (DOI:10.1109/JBHI.2022.3216446)
  5. Yu Fu#, Peng Xue, Enqing Dong*, “Densely Connected Attention Network for Diagnosing COVID-19 Based on Chest CT,” Computers in Biology and Medicine, Volume 137, September 2021. (SCI: IF=6.698, JCR Q1) (DOI:10.1016/j.compbiomed.2021.104857)
  6. Yu Fu#, Peng Xue#, Ning Li#, Peng Zhao, Zhuodong Xu, Huizhong Ji, Wentao Cui, Enqing Dong, “Fusion of 3D Lung CT and Serum Biomarkers for Diagnosis of Multiple Pathological Types on Pulmonary Nodules,” Computer Methods and Programs in Biomedicine, Volume 210, October 2021. (SCI: IF=7.022, JCR Q1) (DOI: 10.1016/j.cmpb.2021.106381)
  7. Yu Fu#, Peng Xue#, Peng Zhao#, Ning Li, Zhuodong Xu, Huizhong Ji, Zhili Zhang, Wentao Cui, Enqing Dong, “3D multi-resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules” International Journal of Imaging Systems and Technology, vol.31, Online, 06 August 2021. (SCI: IF=2.177, JCR Q3) ( DOI: 10.1002/ima.22642)
  8. Yu Fu#, Peng Xue#, Meirong Ren, Enqing Dong*, “Harmony Loss for Unbalanced Prediction,” IEEE Journal of Biomedical and Health Informatics, vol.26(2):828-83,2022. (SCI: IF=7.021, JCR Q1) ( DOI: 10.1109/JBHI.2021.3094578)
  9. Yu Fu#, Peng Xue#, Huizhong Ji, Wentao Cui, Enqing Dong, “Deep Model with Siamese Network for Viable and Necrotic Tumor Regions Assessment in Osteosarcoma,” Medical Physics, vol.47(10):4895-4905,2020. (SCI: IF=4.506, JCR Q1) ( DOI: doi.org/10.1002/mp.14397)
  10. Yu Fu#, Guoxiang Wang, Zhe An, Ting Dong, Zhifeng Gao*, “Automatic Colorization Based on Deep Learning,” International Symposium on Computational Intelligence and Industrial Applications, 2019. (EI)
  11. Peng Xue#, Yu Fu#, Jingyang Zhang, Lei Ma, Meirong Ren, Zhili Zhang, Enqing Dong*, “Effective lung ventilation estimation based on 4D CT image registration and supervoxels,” Biomedical Signal Processing and Control, vol.79, Online, 28 August 2022. (SCI: IF=5.076, JCR Q1) ( DOI: doi.org/10.1016/j.bspc.2022.104074) (共同一作)
  12. Zhili Zhang#, Taohui Xiao#, Yu Fu#, Yuqiang Gao#, Meirong Ren, Wentao Cui, Enqing Dong, “3D Multi-Resolution Attention Capsule Network for Diagnosing Multi-Pathological Types of Pulmonary Nodules,” International Journal of Imaging Systems and Technology, vol.31, Online, 06 August 2021. (SCI: IF=2.177, JCR Q3) ( DOI: doi.org/10.1002/ima.22726) (共同一作)
  13. Peng Xue#, Yu Fu, Huizhong Ji, Wentao Cui, Enqing Dong, “Lung Respiratory Motion Estimation Based on Fast Kalman Filtering and 4D CT Image Registration,” IEEE Journal of Biomedical and Health Informatics, vol.39, Oct. 2020. (SCI: IF=7.021, JCR Q1) ( DOI: doi.org/10.1109/JBHI.2020.3030071)

在投文章

  1. Yu Fu#, AdveDiffNet: Adversarial Diffusion Network for Unbalanced Melanoma Diagnosis.

发明专利

  1. 基于人工智能融合多模态信息构建肺结节良恶性的多种病理类型的诊断模型
  2. 基于三维深度学习网络预测肺结节良恶性的多病理类型的分类方法
  3. 基于卡尔曼滤波和4D CT 图像配准的肺部呼吸运动估计方法
  4. 构建肺结节良恶性的多种病理类型诊断模型的方法

荣誉奖励

项目研究