arXiv:2510.23504v1 Announce Type: new 
Abstract: 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.

تم تقديم نهج جديد يسمى iPac لتحسين أداء الشبكات العصبية الرسومية في تصنيف الصور الطبية. من خلال دمج سياق الباتشات داخل الصورة، تعزز هذه الطريقة فهم الهياكل والعلاقات الأساسية داخل الصور. هذه الخطوة مهمة لأنها قد تؤدي إلى تشخيصات أكثر دقة ونتائج أفضل للمرضى في المجال الطبي.

Se ha presentado un nuevo enfoque llamado iPac para mejorar el rendimiento de las redes neuronales gráficas en la clasificación de imágenes médicas. Al incorporar el contexto de parches intra-imagen, este método mejora la comprensión de las estructuras subyacentes y las relaciones dentro de las imágenes. Este avance es significativo, ya que podría conducir a diagnósticos más precisos y mejores resultados para los pacientes en el campo médico.

Une nouvelle approche appelée iPac a été introduite pour améliorer les performances des réseaux de neurones graphiques dans la classification d'images médicales. En intégrant le contexte des patchs intra-image, cette méthode améliore la compréhension des structures sous-jacentes et des relations au sein des images. Cette avancée est significative car elle pourrait conduire à des diagnostics plus précis et à de meilleurs résultats pour les patients dans le domaine médical.

A new approach called iPac has been introduced to improve the performance of graph neural networks in medical image classification. By incorporating intra-image patch context, this method enhances the understanding of the underlying structures and relationships within images. This advancement is significant as it could lead to more accurate diagnoses and better patient outcomes in the medical field.

iPac: Incorporating Intra-image Patch Context into Graph Neural Networks for Medical Image Classification

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