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

Yashbir Singh¹, Yassine Himeur², Colleen M Farrelly³

¹Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.

Bioengineering (Basel, Switzerland)

|June 26, 2026

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This summary is machine-generated.

Graph neural networks (GNNs) enhance medical imaging and biological data analysis by modeling complex relationships. Clinical translation requires addressing challenges in graph construction, validation, and interpretability for GNNs.

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.

Keywords:

biological data generative AI geometric deep learning graph neural networks graph transformers medical imaging analysis radiomics topological data analysis

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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.