Abstract
Alzheimer’s disease (AD) is the most common age-related dementia, which significantly affects an individual’s daily life and impact socioeconomics. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a non-invasive biomarker to detect brain aging. Previous evidence shows that the structural brain network generated from the diffusion MRI promises to classify dementia accurately based on deep learning models. However, the limited availability of diffusion MRI challenges the model training of deep learning. We propose the BrainNetGAN, a variant of the generative adversarial network, to efficiently augment the structural brain networks for dementia classifying tasks. The BrainNetGAN model is trained to generate fake brain connectivity matrices, which are expected to reflect the latent distribution and topological features of the real brain network data. Numerical results show that the BrainNetGAN outperforms the benchmarking algorithms in augmenting the brain networks for AD classification tasks.
C. Li and Y. Wei—Equal Contribution.
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Li, C., Wei, Y., Chen, X., Schönlieb, CB. (2021). BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dementia Classification. In: Engelhardt, S., et al. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. DGM4MICCAI DALI 2021 2021. Lecture Notes in Computer Science(), vol 13003. Springer, Cham. https://doi.org/10.1007/978-3-030-88210-5_9
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DOI: https://doi.org/10.1007/978-3-030-88210-5_9
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