HyperAMS-Net：用于脑疾病分类的自适应多尺度空间超图网络
HyperAMS-Net: Adaptive Multi-Scale Spatial Hypergraph Network for Brain Disorder Classification
- University of North Texas（北德克萨斯大学）
- Inje University（仁济大学）
- Universiti Malaysia Perlis（马来西亚玻璃市大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对脑疾病分类中受试者异质性和多尺度模式挑战，提出HyperAMS-Net框架，整合自适应多尺度卷积、超图注意力等模块，在三个基准数据集上取得最优性能。
AI中文摘要：
由于受试者间存在显著的异质性，以及功能连接和形态学表征中呈现的复杂多尺度模式，从神经影像数据中准确分类脑疾病仍然具有挑战性。为解决这些挑战，我们提出了HyperAMS-Net，一种利用静息态功能磁共振成像或结构磁共振成像衍生的神经影像表征进行脑疾病分类的深度学习框架。HyperAMS-Net整合了自适应多尺度卷积、超图注意力、空间-通道注意力和自适应特征融合。具体而言，自适应多尺度卷积在多个感受野上学习数据驱动的权重，以捕获不同尺度上的互补模式。超图注意力通过节点-超边-节点消息传递来建模所学特征表示之间的高阶依赖关系，而空间-通道注意力则增强判别性特征学习。自适应特征融合进一步聚合并行网络分支之间的互补信息。HyperAMS-Net在三个涵盖不同脑疾病的基准数据集上进行了评估：用于自闭症谱系障碍的ABIDE、用于重度抑郁症的REST-meta-MDD以及用于阿尔茨海默病的ADNI，采用5折分层交叉验证。HyperAMS-Net在所有评估数据集上均取得了最先进的性能，在比较方法中获得了最高的准确率和AUC。消融研究进一步证明了每个提出组件的贡献，其中移除超图注意力时观察到最大的性能下降。
英文摘要：
Accurate classification of brain disorders from neuroimaging data remains challenging because of substantial inter-subject heterogeneity and the complex multi-scale patterns present in functional connectivity and morphological representations. To address these challenges, we propose HyperAMS-Net, a deep learning framework for brain disorder classification using neuroimaging representations derived from resting-state functional MRI or structural MRI. HyperAMS-Net integrates adaptive multi-scale convolution, hypergraph attention, spatial-channel attention, and adaptive feature fusion. Specifically, adaptive multi-scale convolution learns data-driven weights over multiple receptive fields to capture complementary patterns at different scales. Hypergraph attention models higher-order dependencies among learned feature representations through node--hyperedge--node message passing, while spatial-channel attention enhances discriminative feature learning. Adaptive feature fusion further aggregates complementary information across parallel network branches. HyperAMS-Net is evaluated on three benchmark datasets spanning distinct brain disorders: ABIDE for autism spectrum disorder, REST-meta-MDD for major depressive disorder, and ADNI for Alzheimer's disease, using 5-fold stratified cross-validation. HyperAMS-Net achieves state-of-the-art performance across all evaluated datasets, attaining the highest accuracy and AUC among the compared methods. Ablation studies further demonstrate the contribution of each proposed component, with the largest performance degradation observed when hypergraph attention is removed.
