论文标题
门:用于时间自我监督学习的图形CCA用于标签有效fMRI分析
GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis
论文作者
论文摘要
在这项工作中,我们使用功能性磁共振成像(fMRI)专注于具有挑战性的任务,神经疾病分类。在基于人群图的疾病分析中,图形卷积神经网络(GCN)取得了显着的成功。但是,这些成就与大量标记的数据密不可分,对虚假信号敏感。为了在标签有效的设置下改善fMRI表示的学习和分类,我们建议在GCN上使用新颖的,理论驱动的自我监督学习(SSL)框架,即在FMRI分析门上用于时间自我监督学习的CCA。具体而言,要求设计一种合适有效的SSL策略,以提取fMRI的形成和鲁棒特征。为此,我们研究了FMRI动态功能连接剂(FC)的几种新的图表增强策略,用于SSL培训。此外,我们利用规范相关分析(CCA)对不同的时间嵌入,并呈现理论含义。因此,这产生了一个新型的两步GCN学习程序,该过程由未标记的fMRI人群图上的(i)SSL组成,并且(ii)在一个小标记的fMRI数据集中进行微调,以进行分类任务。我们的方法在两个独立的fMRI数据集上进行了测试,这表明自闭症和痴呆症诊断方面表现出色。
In this work, we focus on the challenging task, neuro-disease classification, using functional magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph convolutional neural networks (GCNs) have achieved remarkable success. However, these achievements are inseparable from abundant labeled data and sensitive to spurious signals. To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE. Concretely, it is demanding to design a suitable and effective SSL strategy to extract formation and robust features for fMRI. To this end, we investigate several new graph augmentation strategies from fMRI dynamic functional connectives (FC) for SSL training. Further, we leverage canonical-correlation analysis (CCA) on different temporal embeddings and present the theoretical implications. Consequently, this yields a novel two-step GCN learning procedure comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning on a small labeled fMRI dataset for a classification task. Our method is tested on two independent fMRI datasets, demonstrating superior performance on autism and dementia diagnosis.