Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution
  for Medical Image Classification
Aryan Singh Pepijn Van de Ven Ciarán Eising Patrick Denny
Graph-based neural network models are gaining traction in the field of
representation learning due to their ability to uncover latent topological
relationships between entities that are otherwise challenging to identify.
These models have been employed across a diverse range of domains, encompassing
drug discovery, protein interactions, semantic segmentation, and fluid dynamics
research. In this study, we investigate the potential of Graph Neural Networks
(GNNs) for medical image classification. We introduce a novel model that
combines GNNs and edge convolution, leveraging the interconnectedness of RGB
channel feature values to strongly represent connections between crucial graph
nodes. Our proposed model not only performs on par with state-of-the-art Deep
Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting
in reduced training time and data requirements. We compare our Graph
Convolutional Neural Network (GCNN) to pre-trained DNNs for classifying
MedMNIST dataset classes, revealing promising prospects for GNNs in medical
image analysis. Our results also encourage further exploration of advanced
graph-based models such as Graph Attention Networks (GAT) and Graph
Auto-Encoders in the medical imaging domain. The proposed model yields more
reliable, interpretable, and accurate outcomes for tasks like semantic
segmentation and image classification compared to simpler GCNNs