BibTex format
@inproceedings{Shehata:2022,
author = {Shehata, N and Bain, W and Glocker, B},
title = {A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging},
url = {http://arxiv.org/abs/2210.16670v1},
year = {2022}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - Graph neural networks have emerged as a promising approach for the analysisof non-Euclidean data such as meshes. In medical imaging, mesh-like data playsan important role for modelling anatomical structures, and shape classificationcan be used in computer aided diagnosis and disease detection. However, with aplethora of options, the best architectural choices for medical shape analysisusing GNNs remain unclear. We conduct a comparative analysis to providepractitioners with an overview of the current state-of-the-art in geometricdeep learning for shape classification in neuroimaging. Using biological sexclassification as a proof-of-concept task, we find that using FPFH as nodefeatures substantially improves GNN performance and generalisation toout-of-distribution data; we compare the performance of three alternativeconvolutional layers; and we reinforce the importance of data augmentation forgraph based learning. We then confirm these results hold for a clinicallyrelevant task, using the classification of Alzheimer's disease.
AU - Shehata,N
AU - Bain,W
AU - Glocker,B
PY - 2022///
TI - A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging
UR - http://arxiv.org/abs/2210.16670v1
ER -