Computer Science and Engineering, Northeastern University, Shenyang, China.
Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China.
Comput Biol Med. 2020 Dec;127:104096. doi: 10.1016/j.compbiomed.2020.104096. Epub 2020 Nov 3.
Recently, brain connectivity networks have been used for the classification of neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease (AD). Network analysis provides a new way for exploring the association between brain functional deficits and the underlying structural disruption related to brain disorders. Network embedding learning that aims to automatically learn low-dimensional representations for brain networks has drawn increasing attention in recent years.
In this work we build upon graph neural network in order to learn useful representations for graph classification in an end-to-end fashion. Specifically, we propose a hierarchical GCN framework (called hi-GCN) to learn the graph feature embedding while considering the network topology information and subject's association at the same time.
To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset. Extensive experiments on ABIDE and ADNI datasets have demonstrated competitive performance of the hi-GCN model. Specifically, we obtain an average accuracy of 73.1%/78.5% as well as AUC of 82.3%/86.5% on ABIDE/ADNI. The comprehensive experiments demonstrate that our hi-GCN is effective for graph classification with brain disorders diagnosis.
The proposed hi-GCN method performs the graph embedding learning from a hierarchical perspective while considering the structure in individual brain network and the subject's correlation in the global population network, which can capture the most essential embedding features to improve the classification performance of disease diagnosis. Moreover, the proposed jointly optimizing strategy also achieves faster training and easier convergence than both the hi-GCN with pre-training and two-step supervision.
最近，脑连接网络已被用于神经障碍的分类，如自闭症谱系障碍（ASD）或阿尔茨海默病（AD）。网络分析为探索与脑障碍相关的脑功能缺陷与潜在结构破坏之间的关联提供了一种新方法。旨在自动学习脑网络低维表示的网络嵌入学习近年来引起了越来越多的关注。
在这项工作中，我们以图神经网络为基础，以端到端的方式学习用于图分类的有用表示。具体来说，我们提出了一种层次图卷积网络框架（称为 hi-GCN），同时考虑网络拓扑信息和主体关联，学习图特征嵌入。
为了证明我们方法的有效性，我们在阿尔茨海默病神经影像学倡议（ADNI）数据集和自闭症脑成像数据交换（ABIDE）数据集上评估了所提出方法的性能。在 ABIDE 和 ADNI 数据集上的广泛实验表明了 hi-GCN 模型的竞争性能。具体来说，我们在 ABIDE/ADNI 上获得了 73.1%/78.5%的平均准确率和 82.3%/86.5%的 AUC。综合实验表明，我们的 hi-GCN 方法对于脑障碍诊断的图分类是有效的。
所提出的 hi-GCN 方法从层次化的角度进行图嵌入学习，同时考虑个体脑网络中的结构和全局人群网络中的主体相关性，可以捕捉到最基本的嵌入特征，以提高疾病诊断的分类性能。此外，所提出的联合优化策略也比具有预训练和两步监督的 hi-GCN 更快的训练和更容易的收敛。
