Despite progress in deep learning for Alzheimer's disease (AD) diagnostics, models trained on structural magnetic resonance imaging (sMRI) often do not perform well when applied to new cohorts due to domain shifts from varying scanners, protocols and patient demographics. AD, the primary driver of dementia, manifests through progressive cognitive and neuroanatomical changes like atrophy and ventricular expansion, making robust, generalizable classification essential for real-world use. While convolutional neural networks and transformers have advanced feature extraction via attention and fusion techniques, single-domain generalization (SDG) remains underexplored yet critical, given the fragmented nature of AD datasets. To bridge this gap, we introduce Extended MixStyle (EM), a framework for blending higher-order feature moments (skewness and kurtosis) to mimic diverse distributional variations. Trained on sMRI data from the National Alzheimer's Coordinating Center (NACC; n=4,647) to differentiate persons with normal cognition (NC) from those with mild cognitive impairment (MCI) or AD and tested on three unseen cohorts (total n=3,126), EM yields enhanced cross-domain performance, improving macro-F1 on average by 2.4 percentage points over state-of-the-art SDG benchmarks, underscoring its promise for invariant, reliable AD detection in heterogeneous real-world settings. The source code will be made available upon acceptance at https://github.com/zobia111/Extended-Mixstyle.

翻译：尽管深度学习在阿尔茨海默病诊断方面取得了进展，但由于扫描设备、成像协议和患者人口统计学差异导致的域偏移，基于结构磁共振成像训练的模型在新队列上应用时往往表现不佳。作为痴呆症的主要病因，阿尔茨海默病通过进行性认知功能衰退和神经解剖学改变（如脑萎缩和脑室扩张）显现，这使得稳健且可泛化的分类方法在实际应用中至关重要。虽然卷积神经网络和Transformer通过注意力机制与融合技术提升了特征提取能力，但鉴于阿尔茨海默病数据集的碎片化特性，单域泛化研究仍显不足却极为关键。为填补这一空白，我们提出了扩展混合风格框架，该框架通过融合高阶特征矩（偏度和峰度）来模拟多样化的分布变化。使用来自国家阿尔茨海默病协调中心的结构磁共振成像数据进行训练，以区分认知正常者与轻度认知障碍或阿尔茨海默病患者，并在三个未见队列上进行测试，扩展混合风格框架展现出增强的跨域性能，其宏平均F1分数较当前最先进的单域泛化基准平均提升2.4个百分点，这凸显了其在异质性现实场景中实现不变且可靠的阿尔茨海默病检测的潜力。源代码将在论文录用后发布于https://github.com/zobia111/Extended-Mixstyle。