Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10340672.
Following the aging of the population, Parkinson's disease (PD) poses a severe challenge to public health. For the diagnosis of PD and the prediction of its progression, numerous computer-aided diagnosis procedures have been developed. Recently, Graph Convolutional Networks (GCN) are widely applied in deep learning to effectively integrate multi-modal features and model subject correlation. However, many GCNs which are used for node classification build large-scale fixed graph topologies using the entire dataset, which could make them impossible to verify independently. Furthermore, past GCN algorithms would need more interpretability, limiting their real-world applications. In this paper, an Interpretable Graph-Learning Convolutional Network (iGLCN) is proposed to enhance the performance of personalized diagnosis for PD while simultaneously producing interpretable results. The proposed method can dynamically adjust the graph structure for GCN to better diagnose outcomes by learning the optimal underlying latent graph. Through interpretable feature learning, the proposed network can interpret diagnosis outcomes. The experiments showed that the proposed method increased flexibility while maintaining a high level of classification performance and could be interpretable for PD diagnosis.Clinical Relevance- The proposed method is expected to have good performance in its strong practicability, feasibility, and interpretability for Parkinson's disease diagnosis.
随着人口老龄化，帕金森病（PD）对公共卫生构成了严峻挑战。为了诊断 PD 和预测其进展，已经开发了许多计算机辅助诊断程序。最近，图卷积网络（GCN）在深度学习中得到了广泛应用，能够有效地整合多模态特征和模型主体相关性。然而，许多用于节点分类的 GCN 使用整个数据集构建大规模固定图拓扑结构，这使得它们不可能独立验证。此外，过去的 GCN 算法需要更高的可解释性，限制了它们在实际应用中的应用。本文提出了一种可解释的图学习卷积网络（iGLCN），以提高 PD 个性化诊断的性能，同时产生可解释的结果。所提出的方法可以通过学习最佳潜在图来动态调整 GCN 的图结构，从而更好地诊断结果。通过可解释的特征学习，所提出的网络可以解释诊断结果。实验表明，该方法在保持高分类性能的同时提高了灵活性，并且可以用于 PD 诊断的可解释性。临床意义-预计该方法在帕金森病诊断方面具有良好的性能，具有很强的实用性、可行性和可解释性。
