A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging. (arXiv:2210.16670v1 [cs.CV])

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 …

arxiv classification graph graph neural networks networks neural networks neuroimaging study