用对比学习提升脑龄估计的鲁棒性与泛化能力
Robust brain age estimation from structural MRI with contrastive learning
- 设计新对比损失函数，利用多中心数据预训练
- 外部数据误差减半，对扫描仪差异不敏感
- 能有效捕捉阿尔茨海默病等加速老化特征
从结构磁共振成像（sMRI）中估算脑龄已成为表征正常与病理衰老的强大工具。本文探索对比学习作为L1监督方法的可扩展且稳健替代方案。提出一种新型对比损失函数$ \mathcal{L}^{exp}$，并在包含超过20,000次扫描的多个公开神经影像数据集上进行评估。实验揭示四项关键发现：第一，大规模多样、多中心数据预训练持续提升泛化性能，使外部均方根误差（MAE）几乎减半；第二，$ \mathcal{L}^{exp}$对站点相关混杂因素具有鲁棒性，随着训练规模增加，扫描仪可预测性保持较低；第三，对比模型在认知障碍及阿尔茨海默病患者中可靠捕获加速老化现象，通过脑龄差分析、ROC曲线和纵向趋势验证；第四，与L1监督基线不同，$ \mathcal{L}^{exp}$维持脑龄估计准确率与下游诊断性能之间的强相关性，支持其作为神经影像基础模型的潜力。这些结果表明，对比学习是构建可泛化且具临床意义脑表征的有前途方向。
原文摘要 · Abstract (English)
Estimating brain age from structural MRI has emerged as a powerful tool for characterizing normative and pathological aging. In this work, we explore contrastive learning as a scalable and robust alternative to L1-supervised approaches for brain age estimation. We introduce a novel contrastive loss function, $\mathcal{L}^{exp}$, and evaluate it across multiple public neuroimaging datasets comprising over 20,000 scans. Our experiments reveal four key findings. First, scaling pre-training on diverse, multi-site data consistently improves generalization performance, cutting external mean absolute error (MAE) nearly in half. Second, $\mathcal{L}^{exp}$ is robust to site-related confounds, maintaining low scanner-predictability as training size increases. Third, contrastive models reliably capture accelerated aging in patients with cognitive impairment and Alzheimer's disease, as shown through brain age gap analysis, ROC curves, and longitudinal trends. Lastly, unlike L1-supervised baselines, $\mathcal{L}^{exp}$ maintains a strong correlation between brain age accuracy and downstream diagnostic performance, supporting its potential as a foundation model for neuroimaging. These results position contrastive learning as a promising direction for building generalizable and clinically meaningful brain representations.
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