论文标题
边缘变化图卷积网络，用于不确定性感知疾病预测
Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
论文作者
论文摘要
对计算模型的需求不断增加，这些模型可以互补地利用不同方式的数据，同时研究受试者之间的关联以进行基于人群的疾病分析。尽管卷积神经网络在表示成像数据方面取得了成功，但这仍然是一项非常具有挑战性的任务。在本文中，我们提出了一个可推广的框架，该框架可以自动将成像数据与人群中的非成像数据整合在一起，以进行不确定性感知疾病的预测。其核心是具有各种边缘的可学习自适应人群图，我们在数学上证明它与图形卷积神经网络相结合。为了估计与图形拓扑相关的预测不确定性，我们提出了蒙特卡洛边缘辍学的新概念。四个数据库的实验结果表明，我们的方法可以始终如一地提高自闭症谱系障碍，阿尔茨海默氏病和眼部疾病的诊断准确性，这表明它在利用计算机辅助诊断的多模态数据方面具有普遍性。
There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis. Despite the success of convolutional neural networks in representation learning for imaging data, it is still a very challenging task. In this paper, we propose a generalizable framework that can automatically integrate imaging data with non-imaging data in populations for uncertainty-aware disease prediction. At its core is a learnable adaptive population graph with variational edges, which we mathematically prove that it is optimizable in conjunction with graph convolutional neural networks. To estimate the predictive uncertainty related to the graph topology, we propose the novel concept of Monte-Carlo edge dropout. Experimental results on four databases show that our method can consistently and significantly improve the diagnostic accuracy for Autism spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its generalizability in leveraging multimodal data for computer-aided diagnosis.
