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

我们研究了基于图神经网络的医学图像分类的潜力。我们提出了一种将图神经网络与边缘卷积相结合的新模型，利用RGB通道特征值的相互连接来强烈表示关键图节点之间的联系。我们的模型在MedMNIST数据集的分类中表现出色，并与现有的深度神经网络相比，参数少1000倍，训练时间和数据需求减少。我们的结果鼓励进一步探索医学影像领域中的其他图神经网络模型。

紧凑而强大：利用图神经网络和边缘卷积进行医学图像分类