  This study highlights the importance of conducting comprehensive model
inspection as part of comparative performance analyses. Here, we investigate
the effect of modelling choices on the feature learning characteristics of
graph neural networks applied to a brain shape classification task.
Specifically, we analyse the effect of using parameter-efficient, shared graph
convolutional submodels compared to structure-specific, non-shared submodels.
Further, we assess the effect of mesh registration as part of the data
harmonisation pipeline. We find substantial differences in the feature
embeddings at different layers of the models. Our results highlight that test
accuracy alone is insufficient to identify important model characteristics such
as encoded biases related to data source or potentially non-discriminative
features learned in submodels. Our model inspection framework offers a valuable
tool for practitioners to better understand performance characteristics of deep
learning models in medical imaging.
