Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes.
Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes.
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DOI：
10.1007/978-3-030-87234-2_48
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Huang, Heng
中科院分区：
文献类型：
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作者：
Zhang, Yanfu;Zhan, Liang;Wu, Shandong;Thompson, Paul;Huang, Heng
弥散MRI衍生的脑结构连接体或脑网络在脑研究中有着广泛的应用。然而，构建大脑网络是高度依赖于各种纤维束成像算法，这导致在决定有关下游分析的最佳视图的困难。在本文中，我们提出从多视图脑网络中学习统一表示。特别是，我们期望学习表征公平地传达来自不同观点的信息，并在一个解脱的意义。我们通过一种使用无监督变分图自动编码器的方法来实现解纠缠。我们通过另一种训练程序实现了视角公平性，即比例性。更具体地说，我们在训练深度网络和网络流问题之间建立了一个类比。在此基础上，提出了一种基于比例性的网络调度算法来实现公平表示学习。实验结果表明，学习的表征适合各种下游任务。它们还表明，所提出的方法有效地保持了相称性。
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.