首页   ·   人员   ·   固定科研人员   ·   副教授、副研究员与青年研究员   ·   正文

人员

  • 单洪明（青年研究员）
  • 研究方向：机器学习、医学图像、影像组学分析、计算机视觉
  • 电子邮箱：hmshan@fudan.edu.cn
  • 个人网站：http://hmshan.io/
  • 简要介绍：Dr. Hongming Shan is a Young Principal Investigator at the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, and also a “Qiusuo” Research Leader at the Shanghai Center for Brain Science and Brain-inspired Technology. Before joining Fudan University in Sep. 2020, he worked with Prof. Ge Wang at the Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, where he was a Postdoctoral Research Associate from Apr. 2017 to May 2020, and a Research Scientist from Jun. 2020 to Aug. 2020. He received his Ph.D. in machine learning from Fudan University in Jan. 2017, under the supervision of Prof. Junping Zhang. He has published research papers in top journals including Nature Machine Intelligence, IEEE Transactions on Cybernetics, IEEE Transactions on Medical Imaging, IEEE Transactions on Information Forensics and Security, and Medical Image Analysis. His research has been covered by several media such as NIH, RPI News, EurekAlert!, Physics world, and HealthImaging.
  • 代表成果：

    1.Shan,H.,Padole,A.,Homayounieh,F.,Kruger,U.,Khera,R.D.,Nitiwarangkul,C.,Kalra,M.K. and Wang,G.,2019. Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. Nature Machine Intelligence,1(6),pp.269-276.

    2.Shan,H.,Zhang,Y.,Yang,Q.,Kruger,U.,Kalra,M.K.,Sun,L.,Cong,W. and Wang,G.,2018. 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network. IEEE Transactions on Medical Imaging,37(6),pp.1522-1534.

    3.Shan,H.,Zhang,J. and Kruger,U.,2018. Framework of randomized distribution features for visual representation and categorization. IEEE Transactions on Cybernetics,49(9),pp.3599-3606.

    4.Shan,H.,Zhang,J. and Kruger,U.,2015. Learning linear representation of space partitioning trees based on unsupervised kernel dimension reduction. IEEE Transactions on Cybernetics,46(12),pp.3427-3438.

    5.Shan,H.*,Jia,X.*,Yan,P.,Li,Y.,Paganetti,H. and Wang,G.,2020. Synergizing medical imaging and radiotherapy with deep learning. Machine Learning: Science and Technology,1(2),021001.(* co-first authors)

上海市浦东新区张江高科技园区张衡路825号复旦大学张江校区

  • 复旦大学类脑智能科学与技术研究院,复旦大学类脑研究院