Graph Convolutional Networks (GCNs) are widely applied in classification tasks by aggregating the neighborhood information of each sample to output robust node embedding. However, conventional GCN methods do not update the graph during the training process so that their effectiveness is always influenced by the quality of the input graph. Moreover, previous GCN methods lack the interpretability to limit their real applications. In this paper, a novel personalized diagnosis technique is proposed for early Alzheimer’s Disease (AD) diagnosis via coupling interpretable feature learning with dynamic graph learning into the GCN architecture. Specifically, the module of interpretable feature learning selects informative features to provide interpretability for disease diagnosis and abandons redundant features to capture inherent correlation of data points. The module of dynamic graph learning adjusts the neighborhood relationship of every data point to output robust node embedding as well as the correlations of all data points to refine the classifier. The GCN module outputs diagnosis results based on the learned inherent graph structure. All three modules are jointly optimized to perform reliable disease diagnosis at an individual level. Experiments demonstrate that our method outputs competitive diagnosis performance as well as provide interpretability for personalized disease diagnosis.

图卷积网络 (GCN) 通过聚合每个样本的邻域信息以输出稳健的节点嵌入，被广泛应用于分类任务。然而，传统的 GCN 方法在训练过程中不会更新图，因此它们的有效性总是受到输入图质量的影响。此外，以前的 GCN 方法缺乏可解释性来限制其实际应用。在本文中，通过将可解释特征学习与动态图学习耦合到 GCN 架构中，提出了一种用于早期阿尔茨海默病 (AD) 诊断的新型个性化诊断技术。具体来说，可解释特征学习模块选择信息特征为疾病诊断提供可解释性，并放弃冗余特征以捕获数据点的内在相关性。动态图学习模块通过调整每个数据点的邻域关系输出鲁棒的节点嵌入以及所有数据点的相关性来细化分类器。GCN 模块根据学习到的固有图结构输出诊断结果。所有三个模块都经过联合优化，以在个体层面进行可靠的疾病诊断。实验表明，我们的方法输出具有竞争力的诊断性能，并为个性化疾病诊断提供可解释性。动态图学习模块通过调整每个数据点的邻域关系输出鲁棒的节点嵌入以及所有数据点的相关性来细化分类器。GCN 模块根据学习到的固有图结构输出诊断结果。所有三个模块都经过联合优化，以在个体层面进行可靠的疾病诊断。实验表明，我们的方法输出具有竞争力的诊断性能，并为个性化疾病诊断提供可解释性。动态图学习模块通过调整每个数据点的邻域关系输出鲁棒的节点嵌入以及所有数据点的相关性来细化分类器。GCN 模块根据学习到的固有图结构输出诊断结果。所有三个模块都经过联合优化，以在个体层面进行可靠的疾病诊断。实验表明，我们的方法输出具有竞争力的诊断性能，并为个性化疾病诊断提供可解释性。