Graph Neural Networks for Medical Imaging Analysis and Biological Data: Integrating Topology, Geometry, Radiomics,...

Area of Science:

• Medical imaging analysis
• Biological data modeling
• Artificial Intelligence (AI)

Background:

• Graph neural networks (GNNs) are increasingly utilized in biomedical fields.
• Integration of radiomics, topology, geometry, and generative AI can improve representation learning.
• GNNs excel at modeling spatial relationships, multimodal interactions, and graph-structured biological networks.

Purpose of the Study:

• To review the application of GNNs in medical imaging and biological data analysis.
• To highlight the capabilities of topology- and geometry-aware GNNs.
• To discuss emerging trends like hybrid graph-transformer and generative graph models.

Main Methods:

• Review of existing literature on GNNs in medical imaging and bioinformatics.
• Analysis of GNNs' strengths in handling non-Euclidean data and complex relationships.
• Examination of advanced GNN architectures and their potential applications.

Main Results:

• GNNs effectively model spatial relationships, multimodal interactions, and biological networks.
• Topology- and geometry-aware GNNs capture multi-scale structure and higher-order relationships.
• Emerging hybrid and generative graph methods show promise for long-range dependencies and data augmentation.

Conclusions:

• GNNs offer significant potential for advancing medical imaging analysis and biological data modeling.
• Clinical translation of GNNs is hindered by graph construction variability, validation issues, and interpretability challenges.
• Future advancements depend on biologically justified graph construction, rigorous validation, and transparent implementation strategies.