The proceedings of GLMI 2019 has been published in LNCS 11849, which is now available online: https://link.springer.com/book/10.1007%2F978-3-030-35817-4

  • Winner of Best Paper Award: Xiaosong Wang, Ling Zhang, Holger R Roth, Daguang Xu, Ziyue Xu, in recognition of their paper entitled “Interactive 3D Segmentation Editing and Refinement via Gated Graph Neural Networks”, Congratulations!
  • Winner of Best Paper Award: Dongren Yao, Mingxia Liu, Mingliang Wang, Chunfeng Lian, Jie Wei, Li Sun, Jing Sui, Dinggang Shen, in recognition of their paper entitled “Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity using Functional MRI“. Congratulations!



Graph learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation and image database retrieval. Graph Learning in Medical Imaging (GLMI 2019) is the 1st workshop on this topic in conjunction with MICCAI 2019, will be held on Oct. 17 (AM), 2019. This workshop focuses on major trends and challenges in this area, and it presents original work aimed to identify new cutting-edge techniques and their applications in medical imaging.

  • Accepted papers will be invited to submit to a special issue of a leading journal with a high impact factor.
  • Accepted papers will be published in LNCS proceeding.
  • GLMI 2019 Best Paper Award will be presented to the best overall scientific paper.

Keynote Speaker

Professor (David) Dagan Feng, Fellow of ACS, HKIE, IET, IEEE, and Australian Academy of Technological Sciences and Engineering.


The main scope of this workshop is to help advance the scientific research within the broad field of graph learning in medical imaging. The technical program will consist of previously unpublished, contributed papers, with substantial time allocated to discussion. We are looking for original, high-quality submissions on innovative researches and developments in the analysis of medical image using graph learning techniques.


Topics of interests include, but are not limited to graph-based methods (e.g., complex network analysis, graph mining, graph learning, graph embedding, kernel methods for structured data, probabilistic and graphical models for structured data, spectral graph methods, machine learning in the context of graphs) with their applications to

  • Medical image analysis
  • Brain connectivity analysis
  • Computer-aided diagnosis
  • Multi-modality fusion
  • Image reconstruction
  • Image retrieval
  • Big medical imaging data analytics
  • Molecular imaging


Best GLMI Paper Award Sponsor ($1,000 Cash Award)

Shanghai QianHu Technology Co., Ltd.

  • Two Best Papers
  • $500 Cash Award for each best paper


Cooperating Organization