  Geometric deep learning can find representations that are optimal for a given
task and therefore improve the performance over pre-defined representations.
  While current work has mainly focused on point representations, meshes also
contain connectivity information and are therefore a more comprehensive
characterization of the underlying anatomical surface.
  In this work, we evaluate four recent geometric deep learning approaches that
operate on mesh representations.
  These approaches can be grouped into template-free and template-based
approaches, where the template-based methods need a more elaborate
pre-processing step with the definition of a common reference template and
correspondences.
  We compare the different networks for the prediction of Alzheimer's disease
based on the meshes of the hippocampus.
  Our results show advantages for template-based methods in terms of accuracy,
number of learnable parameters, and training speed.
  While the template creation may be limiting for some applications,
neuroimaging has a long history of building templates with automated tools
readily available.
  Overall, working with meshes is more involved than working with simplistic
point clouds, but they also offer new avenues for designing geometric deep
learning architectures.
