论文标题
多站点大脑年龄预测中回归的对比度学习
Contrastive learning for regression in multi-site brain age prediction
论文作者
论文摘要
在脑年龄预测中建立准确的深度学习（DL）模型是神经影像学中非常相关的主题，因为它可以帮助更好地了解神经退行性疾病并找到新的生物标志物。为了估计准确且可推广的模型，已经收集了大型数据集，这些数据集通常是多站点和多扫描仪。这种较大的异质性会对DL模型的概括性能产生负面影响，因为它们容易过度拟合与位点相关的噪声。最近，对比度学习方法已被证明对数据或标签中的噪声更为强大。因此，我们提出了使用MRI扫描来预测可靠的大脑年龄预测的新型对比度学习回归损失。我们的方法在OpenBHB挑战中实现了最先进的表现，从而产生了与现场相关噪声的最佳概括能力和鲁棒性。
Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and find new biomarkers. To estimate accurate and generalizable models, large datasets have been collected, which are often multi-site and multi-scanner. This large heterogeneity negatively affects the generalization performance of DL models since they are prone to overfit site-related noise. Recently, contrastive learning approaches have been shown to be more robust against noise in data or labels. For this reason, we propose a novel contrastive learning regression loss for robust brain age prediction using MRI scans. Our method achieves state-of-the-art performance on the OpenBHB challenge, yielding the best generalization capability and robustness to site-related noise.
