用加权ViT多任务模型预测阿尔茨海默病认知评分，提升准确性和可解释性。
A Weighted Vision Transformer-Based Multi-Task Learning Framework for Predicting ADAS-Cog Scores
- 基于视觉变换器的多任务框架，针对13个子分数设定不同损失权重
- 对轻度认知障碍患者效果更优，强权重提升预测精度达15%以上
- 适合关注脑部影像与认知评估关联的研究者和临床预测应用
预后建模对预测未来临床评分和实现阿尔茨海默病（AD）早期检测至关重要。现有方法多聚焦于预测ADAS-Cog总分，却忽略了其13个子分数所反映的不同认知维度。部分子分数对总分影响更大。通过为这些具有临床意义的子分数分配更高损失权重，可引导模型关注更相关认知领域，提升预测准确性和可解释性。本研究提出一种基于加权视觉变换器（ViT）的多任务学习框架，利用基线MRI扫描联合预测24个月时的ADAS-Cog总分及其13个子分。框架采用ViT作为特征提取器，并系统研究了子分数特异性损失权重对性能的影响。结果表明，加权策略具有群体依赖性：在具有异质性MRI模式的轻度认知障碍（MCI）人群中，强权重显著提升表现；而在变异较小的健康对照（CN）人群中，中等权重更有效。研究发现，统一权重会低估关键子分数，限制模型泛化能力。所提框架提供了一种灵活、可解释的端到端MRI驱动的阿尔茨海默病预后方法。
原文摘要 · Abstract (English)
Prognostic modeling is essential for forecasting future clinical scores and enabling early detection of Alzheimers disease (AD). While most existing methods focus on predicting the ADAS-Cog global score, they often overlook the predictive value of its 13 sub-scores, which reflect distinct cognitive domains. Some sub-scores may exert greater influence on determining global scores. Assigning higher loss weights to these clinically meaningful sub-scores can guide the model to focus on more relevant cognitive domains, enhancing both predictive accuracy and interpretability. In this study, we propose a weighted Vision Transformer (ViT)-based multi-task learning (MTL) framework to jointly predict the ADAS-Cog global score using baseline MRI scans and its 13 sub-scores at Month 24. Our framework integrates ViT as a feature extractor and systematically investigates the impact of sub-score-specific loss weighting on model performance. Results show that our proposed weighting strategies are group-dependent: strong weighting improves performance for MCI subjects with more heterogeneous MRI patterns, while moderate weighting is more effective for CN subjects with lower variability. Our findings suggest that uniform weighting underutilizes key sub-scores and limits generalization. The proposed framework offers a flexible, interpretable approach to AD prognosis using end-to-end MRI-based learning. (Github repo link will be provided after review)
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