Geometric deep learning for personalized medicine
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Description:
In recent years, artificial intelligence has had a major impact on the automatic analysis of medical image data. However, while a lot of machine learning research has focused on regular 2D or 3D image data, these methods do not generalize to learning from 3D representations extracted from medical images. Recently, geometric deep learning and in particular graph neural networks have emerged as an exciting new direction for machine learning models on such representations.
In this project, we want to expand and apply graph neural networks to learn on 3D shape representations extracted from medical data. We take a new angle at problems in computational science and accelerate the computation of structural dynamics and fluid flow. Since the underlying physical mechanisms of human arteries and organs are well-studied, we also incorporate existing knowledge in our models.
Additionally, we address the question of how deep neural networks and in particular graph neural networks can be described and studied with mathematical rigor. Thereby, we hope to not only find out how deep learning can help in the computational simulation of fluids and solids, but also how the well-established knowledge of numerical analysis can help us design better deep neural networks.
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