Abstract
Deep learning models, such as convolutional neural networks and self-attention mechanisms, have been shown to be effective in computer-aided diagnosis (CAD) of Alzheimer’s disease (AD) using structural magnetic resonance imaging (sMRI). Most of them use spatial convolutional filters to learn local information from the images. In this paper, we propose a 3D Global Fourier Network (GF-Net) to utilize global frequency information that captures long-range dependency in the spatial domain. The GF-Net contains three primary components: a 3D discrete Fourier transform, an element-wise multiplication between frequency domain features and learnable global filters, and a 3D inverse Fourier transform. The GF-Net is trained by a multi-instance learning strategy to identify discriminative features. Extensive experiments on two independent datasets (ADNI and AIBL) demonstrate that our proposed GF-Net outperforms several state-of-the-art methods in terms of accuracy and other metrics, and can also identify pathological regions of AD. The code is released at https://github.com/qbmizsj/GFNet.
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Acknowledgments
This work was supported in part by Science and Technology Commission of Shanghai Municipality (20ZR1407800), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), and Fudan Univerisity startup fund (JIH2305006Y).
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Zhang, S. et al. (2022). 3D Global Fourier Network for Alzheimer’s Disease Diagnosis Using Structural MRI. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_4
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