A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging

Nairouz Shehata, Wulfie Bain, Ben Glocker

Keywords: Shape classification, graph neural networks, brain structures, 3D mesh data

TL;DR: We evaluate and compare different architectural and model choices for graph neural networks applied to shape classification.

Abstract: Graph neural networks have emerged as a promising approach for the analysis of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays an important role for modelling anatomical structures, and shape classification can be used in computer aided diagnosis and disease detection. However, with a plethora of options, the best architectural choices for medical shape analysis using GNNs remain unclear. We conduct a comparative analysis to provide practitioners with an overview of the current state-of-the-art in geometric deep learning for shape classification in neuroimaging. Using biological sex classification as a proof-of-concept task, we find that using FPFH as node features substantially improves GNN performance and generalisation to out-of-distribution data; we compare the performance of three alternative convolutional layers; and we reinforce the importance of data augmentation for graph based learning. We then confirm these results hold for a clinically relevant task, using the classification of Alzheimer's disease.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.16670/code)