In this study, we present the Graph Sub-Graph Network (GSN), a novel hybrid image classification model merging the strengths of Convolutional Neural Networks (CNNs) for feature extraction and Graph Neural Networks (GNNs) for structural modeling. GSN employs k-means clustering to group graph nodes into clusters, facilitating the creation of subgraphs. These subgraphs are then utilized to learn representative `atoms` for dictionary learning, enabling the identification of sparse, class-distinguishable features. This integrated approach is particularly relevant in domains like medical imaging, where discerning subtle feature differences is crucial for accurate classification. To evaluate the performance of our proposed GSN, we conducted experiments on benchmark datasets, including PascalVOC and HAM10000. Our results demonstrate the efficacy of our model in optimizing dictionary configurations across varied classes, which contributes to its effectiveness in medical classification tasks. This performance enhancement is primarily attributed to the integration of CNNs, GNNs, and graph learning techniques, which collectively improve the handling of datasets with limited labeled examples. Specifically, our experiments show that the model achieves a higher accuracy on benchmark datasets such as Pascal VOC and HAM10000 compared to conventional CNN approaches.

翻译：本研究提出了一种新颖的混合图像分类模型——图子图网络（GSN），该模型融合了卷积神经网络（CNN）在特征提取和图神经网络（GNN）在结构建模方面的优势。GSN采用k-means聚类算法对图节点进行聚类分组，从而促进子图的构建。这些子图随后被用于学习代表性“原子”以进行字典学习，从而能够识别稀疏且具有类别区分性的特征。这种集成方法在医学成像等领域尤为重要，因为识别细微的特征差异对于精确分类至关重要。为了评估所提出的GSN的性能，我们在基准数据集（包括PascalVOC和HAM10000）上进行了实验。我们的结果表明，该模型在优化不同类别的字典配置方面具有显著效果，这有助于提升其在医学分类任务中的有效性。这种性能提升主要归因于CNN、GNN和图学习技术的集成，这些技术共同改善了在有限标注样本数据集上的处理能力。具体而言，我们的实验显示，与传统的CNN方法相比，该模型在Pascal VOC和HAM10000等基准数据集上实现了更高的准确率。