针对阿尔茨海默病诊断中多模态数据缺失问题，提出自适应蒸馏框架提升模型鲁棒性。
PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis
- 基于原型匹配增强缺失模态表征，动态采样平衡训练
- 在20%~70%缺失率下显著优于现有方法
- 适合临床实际中数据不全的阿尔茨海默病诊断场景
阿尔茨海默病诊断中常因成本与临床限制导致多模态数据缺失，而现有方法多仅用完整数据训练，忽略大量不完整样本，削弱了有效训练集规模。尽管部分方法尝试纳入不完整数据，却难以解决高缺失率下的模态间特征对齐与知识迁移问题。为此，我们提出原型引导的自适应蒸馏（PGAD）框架，直接将不完整多模态数据纳入训练。PGAD通过原型匹配增强缺失模态表示，并采用动态采样策略平衡学习过程。在包含不同缺失率（20%、50%、70%）的ADNI数据集上验证，结果表明其显著优于现有先进方法。消融实验确认原型匹配与自适应采样机制的有效性，凸显该框架在真实临床环境中实现鲁棒、可扩展的阿尔茨海默病诊断潜力。
原文摘要 · Abstract (English)
Missing modalities pose a major issue in Alzheimer's Disease (AD) diagnosis, as many subjects lack full imaging data due to cost and clinical constraints. While multi-modal learning leverages complementary information, most existing methods train only on complete data, ignoring the large proportion of incomplete samples in real-world datasets like ADNI. This reduces the effective training set and limits the full use of valuable medical data. While some methods incorporate incomplete samples, they fail to effectively address inter-modal feature alignment and knowledge transfer challenges under high missing rates. To address this, we propose a Prototype-Guided Adaptive Distillation (PGAD) framework that directly incorporates incomplete multi-modal data into training. PGAD enhances missing modality representations through prototype matching and balances learning with a dynamic sampling strategy. We validate PGAD on the ADNI dataset with varying missing rates (20%, 50%, and 70%) and demonstrate that it significantly outperforms state-of-the-art approaches. Ablation studies confirm the effectiveness of prototype matching and adaptive sampling, highlighting the potential of our framework for robust and scalable AD diagnosis in real-world clinical settings.
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