Graph neural networks for image‐guided disease diagnosis: A review

• Published in: iRadiology (Online) Vol. 1; no. 2; pp. 151 - 166
• Main Authors: Zhang, Lin, Zhao, Yan, Che, Tongtong, Li, Shuyu, Wang, Xiuying
• Format: Journal Article
• Language: English
• Published: Beijing John Wiley & Sons, Inc 01.06.2023; Wiley
• Subjects: Algorithms; Alzheimer's disease; Brain research; Deep learning; Disease; disease diagnosis; graph neural networks; Graph representations; Medical diagnosis; medical imaging; Multiple sclerosis; Neural networks; review
• ISSN: 2834-2879; 2834-2860; 2834-2879
• DOI: 10.1002/ird3.20

Medical imaging is playing an increasingly crucial role in disease diagnosis. Numerous deep learning‐based methods have been developed for image‐guided automatic disease diagnosis. Most of the methods have harnessed conventional convolutional neural networks, which are directly applied in the regular image domain. However, some irregular spatial patterns revealed in medical images are also critical to disease diagnosis, since they can describe latent relations in different image regions of a subject (e.g., different focal lesions in an image) or between different groups (e.g., Alzheimer's disease and healthy control). Therefore, how to exploit and analyze irregular spatial patterns and their relations has become a research challenge in the field of image‐guided disease diagnosis. To address this challenge, graph neural networks (GNNs) are proposed to perform the convolution operation on graphs. Graphs can naturally represent irregular spatial structures. Because of their ability to aggregate node features, edge features, and graph structure information to capture and learn hidden spatial patterns in irregular structures, GNN‐based algorithms have achieved promising results in the detection of various diseases. In this paper, we introduce commonly used GNN‐based algorithms and systematically review their applications to disease diagnosis. We summarize the workflow of GNN‐based applications in disease diagnosis, ranging from localizing the regions of interest and edge construction to modeling. Furthermore, we discuss the limitations and outline potential research directions for GNNs in disease diagnosis. Graph Neural Network (GNN)‐based algorithms have achieved promising results in the detection of various diseases, due to the ability of capturing the hidden spatial patterns in irregular structures, by aggregating the node features, edge features, and graph structure information. This paper systematically reviews common‐used GNN‐based algorithms for image‐based disease diagnosis, including general workflow, limitations and further directions.