用图神经网络分析脑白质数据，提升阿尔茨海默病和额颞叶痴呆的早期识别准确率
ARMARecon: An ARMA Convolutional Filter based Graph Neural Network for Neurodegenerative Dementias Classification
- 基于自回归滑动平均滤波器构建图网络，捕捉脑区间局部与全局连接模式
- 在ADNI和NIFD数据集上分类准确率超越现有最优方法
- 利用20个分组的各向异性分数直方图特征，缓解过度平滑问题
阿尔茨海默病（AD）和额颞叶痴呆（FTD）的早期检测对降低疾病进展风险至关重要。由于这两种疾病沿白质区域以全局、图依赖的方式传播，基于图的神经网络非常适合捕捉此类模式。因此，我们提出ARMARecon，一个融合自回归滑动平均（ARMA）图滤波与重建驱动目标的统一图学习框架，以增强特征表示并提高分类准确性。ARMARecon通过提取白质区域的20个分组各向异性分数（FA）直方图特征，有效建模局部与全局连通性，同时缓解过平滑问题。在多中心dMRI数据集ADNI和NIFD上，ARMARecon的表现优于当前最先进的方法。
原文摘要 · Abstract (English)
Early detection of neurodegenerative diseases such as Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is essential for reducing the risk of progression to severe disease stages. As AD and FTD propagate along white-matter regions in a global, graph-dependent manner, graph-based neural networks are well suited to capture these patterns. Hence, we introduce ARMARecon, a unified graph learning framework that integrates Autoregressive Moving Average (ARMA) graph filtering with a reconstruction-driven objective to enhance feature representation and improve classification accuracy. ARMARecon effectively models both local and global connectivity by leveraging 20-bin Fractional Anisotropy (FA) histogram features extracted from white-matter regions, while mitigating over-smoothing. Overall, ARMARecon achieves superior performance compared to state-of-the-art methods on the multi-site dMRI datasets ADNI and NIFD.
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