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

TL;DR

iPac introduces a novel graph-based method that enhances medical image classification by capturing structural relationships among visual patches, leading to significant accuracy improvements.

Contribution

The paper proposes a unified framework that constructs meaningful graph representations from image patches, improving graph neural network performance in medical image classification.

Findings

Up to 5% accuracy improvement over baselines

Effective capture of image structure and relationships

Versatile approach applicable to various medical datasets

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.