Graph neural networks have emerged as a promising paradigm for image processing, yet their performance in image classification tasks is hindered by a limited consideration of the underlying structure and relationships among visual entities. This work presents iPac, a novel approach to introduce a new graph representation of images to enhance graph neural network image classification by recognizing the importance of underlying structure and relationships in medical image classification. iPac integrates various stages, including patch partitioning, feature extraction, clustering, graph construction, and graph-based learning, into a unified network to advance graph neural network image classification. By capturing relevant features and organising them into clusters, we construct a meaningful graph representation that effectively encapsulates the semantics of the image. Experimental evaluation on diverse medical image datasets demonstrates the efficacy of iPac, exhibiting an average accuracy improvement of up to 5% over baseline methods. Our approach offers a versatile and generic solution for image classification, particularly in the realm of medical images, by leveraging the graph representation and accounting for the inherent structure and relationships among visual entities.

翻译：图神经网络已成为图像处理领域一种有前景的范式，但其在图像分类任务中的性能受到对视觉实体底层结构和关系考虑不足的限制。本文提出iPac，一种通过识别医学图像分类中底层结构和关系的重要性，引入新的图像图表示以增强图神经网络图像分类的新方法。iPac将块划分、特征提取、聚类、图构建和图学习等多个阶段集成到一个统一网络中，以推进图神经网络图像分类。通过捕获相关特征并将其组织成簇，我们构建了一个有意义的图表示，有效封装了图像的语义信息。在多种医学图像数据集上的实验评估证明了iPac的有效性，其平均准确率相比基线方法提升最高达5%。我们的方法通过利用图表示并考虑视觉实体之间的固有结构和关系，为图像分类（尤其是在医学图像领域）提供了一种通用且灵活的解决方案。