MICCAI 2019 Tutorial

Hypergraph Learning: Methods, Tools and Applications in Medical Image Analysis

MICCAI 2019 Tutorial


Sunday, 13 October 2019

Location information: InterContinental Shenzhen, China


In recent years, the MICCAI community has witnessed a booming increase of machine learning technique for medical image analysis. Among these, how to build data correlation and conduct classification is one important task. Hypergraph, as an effective data modelling and learning method, has shown its effectiveness in many tasks, such as Alzheimer's Disease diagnosis, brain structure segmentation, medical image retrieval and brain network modelling.

In this tutorial, we will introduce hypergraph learning methods, applications and tools in medical image analysis field. The topics covered in this tutorial include hypergraph preliminaries and hypergraph generation methods, recent progress in hypergraph learning, the applications of hypergraph learning in medical image segmentation, retrieval and diagnosis, and the toolbox of hypergraph learning which can be used in medical image analysis.

Tutorial Outline

  1. Preliminaries of hypergraph and hypergraph generation methods
    • The basic knowledge about hypergraph
    • The comparison between hypergraph and graph
    • Hypergraph generation methods
    • Typical hypergraph modelling methods
  2. Recent hypergraph learning methods
    • Traditional transductive learning on hypergraph
    • Traditional inductive learning on hypergraph
    • The dynamic hypergraph structure learning method
    • Recent hypergraph neural network methods
  3. Applications in medical image analysis
    • Mild cognitive impairment diagnosis
    • Medical image retrieval
    • Brain structure segmentation
  4. The toolbox for hypergraph learning

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