Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis

Background and objective: Machine learning frameworks have demonstrated their potentials in dealing with complex data structures, achieving remarkable results in many areas, including brain imaging. However, a large collection of data is needed to train these models. This is particularly challenging in the biomedical domain since, due to acquisition accessibility, costs and pathology related variability, available datasets are limited and usually imbalanced. To overcome this challenge, generative models can be used to generate new data. Methods: In this study, a framework based on generative adversarial network is proposed to create synthetic structural brain networks in Multiple Sclerosis (MS). The dataset consists of 29 relapsing-remitting and 19 secondary-progressive MS patients. T1 and diffusion tensor imaging (DTI) acquisitions were used to obtain the structural brain network for each subject. Evaluation of the quality of newly generated brain networks is performed by (i) analysing their structural properties and (ii) studying their impact on classification performance. Results: We demonstrate that advanced generative models could be directly applied to the structural brain networks. We quantitatively and qualitatively show that newly generated data do not present significant differences com pared to the real ones. In addition, augmenting the existing dataset with generated samples leads to an improvement of the classification performance (F1(score) 81%) with respect to the baseline approach (F1(score) 66%). Conclusions: Our approach defines a new tool for biomedical application when connectome-based data augmentation is needed, providing a valid alternative to usual image-based data augmentation techniques. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a framework based on generative adversarial network to create synthetic structural brain networks in Multiple Sclerosis (MS). The quality of generated data is comparable to real data, and augmenting the existing dataset with generated samples leads to an improvement in classification performance.