用笔迹分析实现高效阿尔茨海默病筛查，模型更小更快。
Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework
- 采用低秩专家混合框架，让多个专家专注不同笔迹特征。
- 推理时仅激活少量参数，诊断准确率超传统模型。
- 适合数字健康场景，为早期筛查提供轻量化方案。
早期可靠检测阿尔茨海默病（AD）对及时临床干预和患者管理至关重要，也支持新疗法评估。本文提出基于笔迹分析的低秩专家混合（LoRA-MoE）深度学习框架用于阿尔茨海默病诊断。笔迹信号作为非侵入性、可扩展的数字生物标志物，能捕捉早期AD相关的细微认知-运动损伤。所提架构使多个专家专注于不同笔迹模式，同时共享基础网络，实现通用表征高效学习并减少专家间干扰。每个专家配备轻量级低秩适配器，显著降低可训练参数数量，提升训练稳定性。在Diagnosis AlzheimeR WIth haNdwriting（DARWIN）数据集上进行评估，开展隐藏层维度、专家数量和LoRA秩等关键参数的消融研究，并与多层感知机（MLP）及传统MoE架构对比。还探索了StackMean和StackMax堆叠集成策略以增强鲁棒性和预测性能。实验表明，LoRA-MoE框架在保持强大诊断性能的同时，推理时激活参数显著更少。结果凸显该方法在笔迹基阿尔茨海默病筛查与数字健康应用中的高精度与计算高效潜力。
原文摘要 · Abstract (English)
Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports the evaluation of emerging therapeutic strategies. In this paper, we propose a Low-Rank Mixture of Experts (LoRA-MoE) deep learning framework for Alzheimer's disease diagnosis based on handwriting analysis. Handwriting signals provide a non-invasive and scalable digital biomarker that captures subtle cognitive-motor impairments associated with early AD progression. The proposed architecture allows multiple experts to specialize in different handwriting patterns while sharing a common base network. This design enables efficient learning of general representations while reducing interference between experts. Each expert is equipped with lightweight low-rank adapters. This mechanism significantly reduces the number of trainable parameters compared with standard Mixture of Experts (MoE) models and improves training stability. The proposed framework is evaluated on the Diagnosis AlzheimeR WIth haNdwriting (DARWIN) dataset. Extensive experiments are conducted, including ablation studies on key architectural parameters such as hidden dimension size, number of experts, and LoRA rank. The method is compared with multilayer perceptron (MLP) and conventional MoE architectures. In addition, stacking ensemble strategies (StackMean and StackMax) are investigated to improve robustness and predictive performance. Experimental results show that the LoRA-MoE framework achieves powerful diagnostic performance while activating significantly fewer parameters during inference. These results highlight the potential of the proposed approach as an accurate and computationally efficient solution for handwriting-based Alzheimer's disease screening and digital health applications.
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