用可解释的神经网络提升阿尔茨海默病早期诊断准确率
GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks
- 将KAN的可学习样条函数嵌入图神经网络，捕捉脑区间非线性关系
- 在ADNI数据集上比传统GCN准确率高4-8%，识别出关键致病脑区
- 适合关注可解释性与精准诊断的医学AI研究者
阿尔茨海默病（AD）是一种复杂的进行性神经退行性疾病，其诊断面临巨大挑战。图卷积网络（GCNs）在建模脑连接方面展现出潜力，但依赖线性变换限制了对神经影像数据中复杂非线性模式的捕捉能力。为此，我们提出GCN-KAN，一种新颖的单模态框架，将柯尔莫戈洛夫-阿诺德网络（KAN）融入GCNs，以提升诊断准确性和可解释性。利用结构磁共振成像（sMRI）数据，模型采用可学习的样条基变换更精准地表达脑区间的相互作用。在阿尔茨海默病神经影像计划（ADNI）数据集上的评估表明，该模型相比传统GCNs分类准确率提升4%-8%，同时提供关键脑区与AD关联的可解释洞察。该方法为早期AD诊断提供了鲁棒且可解释的工具。
原文摘要 · Abstract (English)
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that poses significant diagnostic challenges due to its complex etiology. Graph Convolutional Networks (GCNs) have shown promise in modeling brain connectivity for AD diagnosis, yet their reliance on linear transformations limits their ability to capture intricate nonlinear patterns in neuroimaging data. To address this, we propose GCN-KAN, a novel single-modal framework that integrates Kolmogorov-Arnold Networks (KAN) into GCNs to enhance both diagnostic accuracy and interpretability. Leveraging structural MRI data, our model employs learnable spline-based transformations to better represent brain region interactions. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, GCN-KAN outperforms traditional GCNs by 4-8% in classification accuracy while providing interpretable insights into key brain regions associated with AD. This approach offers a robust and explainable tool for early AD diagnosis.
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