π Official Launch of MultiGeoMed
We are thrilled to announce the official launch of MultiGeoMed β a cutting-edge, multidisciplinary research project at the intersection of artificial intelligence, medical imaging, and geometric deep learning.
MultiGeoMed aims to advance fundamental knowledge in multimodal geometric deep learning, a rapidly evolving field that explores how different geometric representations can be used together to improve performance in medical imaging tasks. Our goal is to push the boundaries of how machines understand and process complex anatomical structures.
π§ What is MultiGeoMed About?
Medical imaging data can be represented in various geometric forms, each offering unique advantages. In this project, we will investigate how to simultaneously leverage multiple geometric representations, including:
- π§ Voxel grids β the standard 3D pixel-based representation used in most medical scans.
- πΈοΈ Surface meshes β capturing the shape and topology of anatomical structures.
- π Point clouds β sparse sets of 3D points representing objects.
- π§ Implicit neural representations β continuous functions learned by neural networks to represent shapes and volumes.
π― Project Objectives
The project will address several key research questions:
- π Which geometric representation provides the most benefit for specific downstream tasks in medical imaging?
- π Can multiple representations be effectively fused to enhance the performance of geometric deep learning models?
- 𧩠What are the most effective strategies for integrating these diverse geometric modalities?
Funding
The project is funded by
National Science Centre, Poland under SONATA Grant no: 2024/55/D/ST6/02081.
It is hosted at the
Sano Science Research Center.