Abstract
Diffusion MRI-derived brain structural connectomes or brain networks are widely used in the brain research. However, constructing brain networks is highly dependent on various tractography algorithms, which leads to difficulties in deciding the optimal view concerning the downstream analysis. In this paper, we propose to learn a unified representation from multi-view brain networks. Particularly, we expect the learned representations to convey the information from different views fairly and in a disentangled sense. We achieve the disentanglement via an approach using unsupervised variational graph auto-encoders. We achieve the view-wise fairness, i.e. proportionality, via an alternative training routine. More specifically, we construct an analogy between training the deep network and the network flow problem. Based on the analogy, the fair representations learning is attained via a network scheduling algorithm aware of proportionality. The experimental results demonstrate that the learned representations fit various downstream tasks well. They also show that the proposed approach effectively preserves the proportionality.
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Acknowledgements
This work was partially supported by NSF IIS 1845666, 1852606, 1838627, 1837956, 1956002, 2045848, IIA 2040588, and NIH U01AG068057, R01AG049371, R01AG071243, RF1MH125928.
The NACC database was funded by NIA U01AG016976. The ADNI data were funded by the Alzheimer’s Disease Metabolomics Consortium (NIA R01AG046171, RF1AG051550 and 3U01AG024904-09S4). The PPMI data were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database.
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Zhang, Y., Zhan, L., Wu, S., Thompson, P., Huang, H. (2021). Disentangled and Proportional Representation Learning for Multi-view Brain Connectomes. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_48
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