  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.
