用对比学习训练图变压器，提升自闭症脑连接分析的检测效果
Self-supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis towards Improving Autism Detection
- 通过随机图扰动和对比学习训练图变压器
- 在ABIDE数据集上达82.6% AUROC和74%准确率
- 适合脑网络分析与自闭症智能诊断研究者
功能磁共振成像（fMRI）可揭示大脑在任务或静息状态下的功能。将fMRI数据表示为相关矩阵，是分析大脑内在连接性的可靠方法。图神经网络（GNN）因具备可解释性，被广泛用于脑网络分析。本文提出一种新型框架，结合对比自监督学习与图变换的图变压器，利用脑网络变压器编码器和随机图扰动进行训练。该方法在自闭症脑影像数据交换（ABIDE）数据集上表现出色，实现82.6%的AUROC和74%的准确率，优于现有最先进方法。
原文摘要 · Abstract (English)
Functional Magnetic Resonance Imaging (fMRI) provides useful insights into the brain function both during task or rest. Representing fMRI data using correlation matrices is found to be a reliable method of analyzing the inherent connectivity of the brain in the resting and active states. Graph Neural Networks (GNNs) have been widely used for brain network analysis due to their inherent explainability capability. In this work, we introduce a novel framework using contrastive self-supervised learning graph transformers, incorporating a brain network transformer encoder with random graph alterations. The proposed network leverages both contrastive learning and graph alterations to effectively train the graph transformer for autism detection. Our approach, tested on Autism Brain Imaging Data Exchange (ABIDE) data, demonstrates superior autism detection, achieving an AUROC of 82.6 and an accuracy of 74%, surpassing current state-of-the-art methods.
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