Abstract
In this paper, we propose a dual-graph learning convolutional network (dGLCN) to achieve interpretable Alzheimer’s disease (AD) diagnosis, by jointly investigating subject graph learning and feature graph learning in the graph convolution network (GCN) framework. Specifically, we first construct two initial graphs to consider both the subject diversity and the feature diversity. We further fuse these two initial graphs into the GCN framework so that they can be iteratively updated (i.e., dual-graph learning) while conducting representation learning. As a result, the dGLCN achieves interpretability in both subjects and brain regions through the subject importance and the feature importance, and the generalizability by overcoming the issues, such as limited subjects and noisy subjects. Experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) datasets show that our dGLCN outperforms all comparison methods for binary classification. The codes of dGLCN are available on https://github.com/xiaotingsong/dGLCN.
T. Xiao and L. Zeng—Equal contribution.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (Grant No. 61876046), Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China (No. ZYGX2022YGRH009 and ZYGX2022YGRH014) and the Guangxi “Bagui” Teams for Innovation and Research, China.
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Xiao, T., Zeng, L., Shi, X., Zhu, X., Wu, G. (2022). Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis. 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 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_39
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