Identifying the progression stages of Alzheimer’s disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have been successfully applied in AD classification. However, these works did not handle the class imbalance issue in classification. Besides, they ignore the heterogeneity of the disease. To this end, we propose a novel cost-sensitive weighted contrastive learning method based on graph convolutional networks (CSWCL-GCNs) for imbalanced AD staging using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed method is developed on a multi-view graph constructed by the functional connectivity (FC) and high-order functional connectivity (HOFC) features of the subjects. A novel cost-sensitive weighted contrastive learning procedure is proposed to capture discriminative information from the minority classes, encouraging the samples in the minority class to provide adequate supervision. Considering the heterogeneity of the disease, the weights of the negative pairs are introduced into contrastive learning and they are computed based on the distance to class prototypes, which are automatically learned from the training data. Meanwhile, the cost-sensitive mechanism is further introduced into contrastive learning to handle the class imbalance issue. The proposed CSWCL-GCN is evaluated on 720 subjects (including 184 NCs, 40 SMC patients, 208 EMCI patients, 172 LMCI patients and 116 AD patients) from the ADNI (Alzheimer’s Disease Neuroimaging Initiative). Experimental results show that the proposed CSWCL-GCN outperforms state-of-the-art methods on the ADNI database.

中文翻译：

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基于图卷积网络的成本敏感加权对比学习对阿尔茨海默病分期的评估


确定阿尔茨海默病 （AD） 的进展阶段可以被视为机器学习中的不平衡多类分类问题。由于阶级失衡问题和疾病的异质性，这是具有挑战性的。近年来，图卷积网络 （GCN） 已成功应用于 AD 分类。然而，这些工作并没有处理分类中的类不平衡问题。此外，他们忽视了疾病的异质性。为此，我们提出了一种基于图卷积网络 （CSWCL-GCN） 的新型成本敏感加权对比学习方法，用于使用静息态功能磁共振成像 （rs-fMRI） 进行不平衡 AD 分期。所提出的方法是在由受试者的功能连接 （FC） 和高阶功能连接 （HOFC） 特征构建的多视图图上开发的。提出了一种新的成本敏感加权对比学习程序来捕获少数群体的判别信息，鼓励少数群体中的样本提供足够的监督。考虑到疾病的异质性，负对的权重被引入对比学习中，并根据到类原型的距离计算它们，这些原型是从训练数据中自动学习的。同时，成本敏感机制被进一步引入对比学习中，以处理类不平衡问题。拟议的 CSWCL-GCN 对来自 ADNI（阿尔茨海默病神经影像学计划）的 720 名受试者（包括 184 名 NC、40 名 SMC 患者、208 名 EMCI 患者、172 名 LMCI 患者和 116 名 AD 患者）进行了评估。实验结果表明，所提出的 CSWCL-GCN 在 ADNI 数据库上的性能优于最先进的方法。