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

翻译：基于图的神经网络模型因其能够揭示实体间潜在拓扑关系（这些关系通常难以识别）而在表示学习领域日益受到关注。这些模型已被广泛应用于药物发现、蛋白质相互作用、语义分割和流体动力学研究等多个领域。在本研究中，我们探讨了图神经网络（GNN）在医学图像分类中的潜力。我们提出了一种融合GNN与边缘卷积的新型模型，利用RGB通道特征值之间的互联性，强有力地表征关键图节点间的连接。我们提出的模型不仅性能可与最先进的深度神经网络（DNN）相媲美，而且参数量减少1000倍，从而降低了训练时间和数据需求。我们将所提出的图卷积神经网络（GCNN）与预训练DNN在MedMNIST数据集分类任务中进行对比，揭示了GNN在医学图像分析中的广阔前景。我们的结果也鼓励在医学成像领域进一步探索先进的基于图的模型，如图注意力网络（GAT）和图自编码器。与更简单的GCNN相比，所提模型在语义分割和图像分类等任务中能产生更可靠、更可解释且更精确的结果。