GraphMedIA 2026 Tutorial at MICCAI 2026

The 4th GraphMedIA tutorial, Graph Learning in Medical Image Analysis, will be held at MICCAI 2026. This half-day tutorial provides a structured overview of graph learning for medical image analysis, covering classical semi-supervised graph methods, graph neural networks, graph transformers, hypergraph learning, graph foundation models, and graph agentic systems.

In this edition, I will lead the Mini Interactive Hands-On Session and Live Demonstration. The session focuses on practical graph and hypergraph learning pipelines for medical imaging, including constructing graph and hypergraph representations from medical imaging data, implementing hypergraph neural networks, training and evaluating models on real-world medical tasks, and exploring structural modelling, message passing, higher-order interactions, scalability, and practical implementation considerations. The hands-on materials will use state-of-the-art open-source hypergraph learning frameworks, including THU-HyperG and DeepHypergraph.

Project Page: GraphMedIA 2026 Tutorial

Tutorial Page: https://math-ml-x.github.io/GraphMedIA26/

MICCAI 2026: https://conferences.miccai.org/2026/en/default.asp