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张荣国
职称：研究员
单位：首都师范大学
社会任职：2018年至今，担任中国生物医学工程学会医学人工智能分会、医学影像工程与技术分会的青年委员；担任Artificial Intelligence In Medicine、Computers in Biology and Medicine等多个学术期刊审稿人；九三学社北京市委医药卫生专委会委员。

个人简介

张荣国，男，博士，研究方向：人工智能技术在医学影像领域的应用。

教育经历

  • ▪2006.9-2012.1 硕博连读，中科院自动化所，模式识别与智能系统专业博士
  • ▪2002.9-2006.7 本科，北京航空航天大学，自动控制与信息技术专业学士

学术贡献

  • 1. Rongguo Zhang*, Chenhao Pei, Ji Shi, Shaokang Wang. Construction and Validation of a General Medical Image Dataset for Pretraining. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01226-3.
  • 2. Chen, C., Zhang, R*. An Ensemble of 2.5D ResUnet Based Models for Segmentation of Kidney and Masses. International Challenge on Kidney and Kidney Tumor Segmentation. Cham: Springer Nature Switzerland, 2023: 47-53.
  • 3. Meng, Qingtao, et al. "Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study." Quantitative Imaging in Medicine and Surgery 13.10 (2023): 6876.（通讯作者）
  • 4. Tang, W., Kang, H., Zhang, H., Yu, P., Arnold, C.W., Zhang, R*. Transformer Lesion Tracker. Medical Image Computing and Computer Assisted Intervention. MICCAI 2022. LNCS, vol 13436, pp.196-206. Springer, Cham. https://doi.org/10.1007/978-3-031- 16446-0_19 （通讯作者）
  • 5. Yu, P., Zhang, H., Kang, H., Tang, W., Arnold, C.W., Zhang, R*. RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans. Medical Image Computing and Computer Assisted Intervention. MICCAI 2022. LNCS, vol 13436, pp. 344-353. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_33 （通讯作者）
  • 6. Tang W, Kang H, Cao Ying, Yu P, Han H, Zhang R* and Chen K. M-SEAM-NAM: Multi-instance Self-supervised Equivalent Attention Mechanism with Neighborhood Affinity Module for Double Weakly Supervised Segmentation of COVID-19. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2021: 262-272.（通讯作者）
  • 7. Chen C, Xu W, Zhang R*. An Efficiency Coarse-to-Fine Segmentation Framework for Abdominal Organs Segmentation[M]//Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation: MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Cham: Springer Nature Switzerland, 2023: 47-55. （通讯作者）
  • 8. Zhang, R., Zhang, H., Wang, S., & Chen, K. (2020, November). Nodule Slices Detection based on Weak Labels with a Novel Deep Learning Method. In 2020 6th International Conference on Robotics and Artificial Intelligence (pp. 1-4).
  • 9. Xu J#, Zhang R#, Zhou Z, et al. Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT. Frontiers in Neuroscience, 2021, 14: 541817. （共同一作）
  • 10. Rongguo Zhang, Zongjian Zhang, Li Li, Liyang Peng. Edge-Enhanced Shape Feature Extraction for Image Retrieval. International Proceedings of Computer Science & Information Tech;2012, Vol. 46, p117-121.
  • 11. Zhang, R., Xiao, B., & Wang, C. (2010, August). Data Transformation of the Histogram Feature in Object Detection. In 2010 20th International Conference on Pattern Recognition (pp. 2893-2896).
  • 12. Zhang, R., Wang, C., & Xiao, B. (2009, October). A strategy of classification via sparse dictionary learned by non-negative K-SVD. In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops (pp. 117-122). IEEE.
  • 13. Zhang, R., Xiao, B., & Wang, C. Oversegment an image to get the candidate detection windows. In 2009 16th IEEE International Conference on Image Processing (ICIP).
  • 14. Xue, Y., Zhang, R., Deng, Y., Chen, K., & Jiang, T. (2017). A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PloS one, 12(6), e0178992.
  • 15. Liang, S., Zhang, R., Liang, D., Song, T., Ai, T., Xia, C., ... & Wang, Y. (2018). Multimodal 3D DenseNet for IDH genotype prediction in gliomas. Genes, 9(8), 382.
  • 16. 刘凯, 张荣国, 涂文婷, 范丽, 邓昱枫, 望云, ... & 刘士远. (2017). 深度学习技术对胸部 X 线平片亚实性结节的检测效能初探. 中华放射学杂志, 51(12), 918-921.
  • 17. Yang, X., Yu, P., Zhang, H., Zhang, R., Liu, Y., Li, H., ... & Yang, Q. (2023). Deep Learning Algorithm Enables Cerebral Venous Thrombosis Detection With Routine Brain Magnetic Resonance Imaging. Stroke.
  • 18. Wu YH, Gao SH, Mei J, Xu J, Fan DP, Zhang RG, Cheng MM. JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation. IEEE Transactions on Image Processing, 2021, 30: 3113-3126. ESI高被引论文、热点论文
  • 19. Wang M, Xia C, Huang L, Xu S, Qin C, Liu J, Cao Y, Yu P, Zhu T, Zhu H, Wu C, Zhang R, Chen X, Wang J, Du G, Zhang C, Wang S, Chen K, Liu Z, Xia L, Wang W. Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation. Lancet Digital Health. 2020 Oct;2(10):e506-e515.
  • 20. Hou ZH, Lu B, Li ZN, An YQ, Gao Y, Yin WH, Liang S, Zhang RG. Machine Learning for Pretest Probability of Obstructive Coronary Stenosis in Symptomatic Patients. JACC Cardiovasc Imaging. 2019 Dec;12(12):2584-2586.

工作经历

  • 2018年至今，中国生物医学工程学会医学人工智能分会、医学影像工程与技术分会的青年委员

  • 2021年至今，北京软件和信息服务业协会特聘专家

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