Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups
Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups
复制标题
DOI：
10.1109/icdmw60847.2023.00050
复制
发表时间：
2023-12
期刊：
影响因子：
--
通讯作者：
Anna Stephens;Francisco Santos;Pang-Ning Tan;A. Esfahanian
中科院分区：
文献类型：
--
作者：
Anna Stephens;Francisco Santos;Pang-Ning Tan;A. Esfahanian
图神经网络（GNN）是一种强大的工具，用于结合成像和非成像医疗信息进行节点分类任务。跨网络节点分类扩展了GNN技术，以考虑域漂移，允许在未标记的目标网络上进行节点分类。在本文中，我们提出了OTGCN，一个强大的，新颖的跨网络节点分类方法。这种方法依靠图卷积网络的概念来利用图数据结构的洞察力，同时应用植根于最佳传输的策略来纠正来自不同数据收集站点的样本之间可能发生的域漂移。这种混合方法为在不同位置和设备上收集许多不同形式数据的场景提供了实用的解决方案。我们证明了这种方法的有效性，在分类自闭症谱系障碍的主题使用混合成像和非成像数据。
Graph neural networks (GNN) are a powerful tool for combining imaging and non-imaging medical information for node classification tasks. Cross-network node classification extends GNN techniques to account for domain drift, allowing for node classification on an unlabeled target network. In this paper we present OTGCN, a powerful, novel approach to cross-network node classification. This approach leans on concepts from graph convolutional networks to harness insights from graph data structures while simultaneously applying strategies rooted in optimal transport to correct for the domain drift that can occur between samples from different data collection sites. This blended approach provides a practical solution for scenarios with many distinct forms of data collected across different locations and equipment. We demonstrate the effectiveness of this approach at classifying Autism Spectrum Disorder subjects using a blend of imaging and non-imaging data.