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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Quantifying the reproducibility of graph neural networks using multigraph data representation.

Ahmed Nebli¹, Mohammed Amine Gharsallaoui², Zeynep Gürler²

¹BASIRA lab, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul, Turkey; National School for Computer Science, University of Manouba, Tunisia.

Neural Networks : the Official Journal of the International Neural Network Society

|February 15, 2022

Summary

This summary is machine-generated.

This study introduces a new framework, RG-Select, to assess the reliability of Graph Neural Networks (GNNs) by quantifying reproducible biomarkers. This ensures trustworthy AI in clinical applications for better diagnosis and prognosis.

Keywords:

Brain biomarkers Brain connectivity multigraphs Graph neural networks Reproducibility

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Area of Science:

Artificial Intelligence
Machine Learning
Biomedical Informatics

Background:

Graph Neural Networks (GNNs) are increasingly used in computer vision and medical diagnosis.
Assessing the reproducibility of GNN-identified biomarkers is crucial for clinical reliability, especially across datasets and disease classes.

Purpose of the Study:

To introduce RG-Select, a novel framework for evaluating GNN reproducibility.
To quantify shared discriminative features (biomarkers) between different GNN models.

Main Methods:

Developed RG-Select, a reproducibility-based GNN selection framework.
Assessed reproducibility by quantifying shared biomarkers across models under varying training strategies and data perturbations.

Main Results:

The RG-Select framework successfully yielded reproducible conclusions across diverse training strategies and clinical datasets.
Demonstrated the framework's robustness despite challenges like data variations.

Conclusions:

RG-Select provides a method for assessing biomarker trustworthiness and reliability in GNNs.
This framework can advance the development of dependable AI tools for computer-aided diagnosis and prognosis.