通过融合高阶特征分布提升阿尔茨海默病诊断模型的跨数据集泛化能力
Higher-Order Domain Generalization in Magnetic Resonance-Based Assessment of Alzheimer's Disease
- 引入扩展混风格方法，混合特征的偏度与峰度以模拟多样分布变化
- 在3个未见队列上平均提升宏观F1值2.4个百分点，优于现有单域泛化方法
- 适用于真实世界中扫描仪、协议差异大的阿尔茨海默病影像分析场景
尽管深度学习在阿尔茨海默病（AD）诊断方面取得进展，基于结构磁共振成像（sMRI）训练的模型在应用于新队列时往往表现不佳，这是由于扫描仪、成像协议和患者人口统计学差异导致的领域偏移。AD作为痴呆的主要病因，表现为认知功能和神经解剖结构的渐进性改变，如脑萎缩和脑室扩大，因此实现稳健且可泛化的分类对实际应用至关重要。尽管卷积神经网络和变换器通过注意力与特征融合技术提升了特征提取能力，但单域泛化（SDG）仍研究不足，尤其考虑到AD数据集的碎片化特性。为此，本文提出扩展混风格（EM）框架，通过混合高阶特征矩（偏度与峰度）来模拟多样分布变化。模型在来自国家阿尔茨海默病协调中心（NACC；n=4,647）的sMRI数据上训练，用于区分正常认知（NC）与轻度认知障碍（MCI）或阿尔茨海默病患者，并在3个未见队列（总计n=3,126）上测试。结果显示，相比最先进的单域泛化基准，EM在跨域性能上平均提升宏观点评分数2.4个百分点，证明其在异构真实场景下实现不变、可靠的AD检测具有潜力。源代码将在接受后公开于https://github.com/zobia111/Extended-Mixstyle。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。