Deep learning techniques have demonstrated great potential for accurately estimating brain age by analyzing Magnetic Resonance Imaging (MRI) data from healthy individuals. However, current methods for brain age estimation often directly utilize whole input images, overlooking two important considerations: 1) the heterogeneous nature of brain aging, where different brain regions may degenerate at different rates, and 2) the existence of age-independent redundancies in brain structure. To overcome these limitations, we propose a Dual Graph Attention based Disentanglement Multi-instance Learning (DGA-DMIL) framework for improving brain age estimation. Specifically, the 3D MRI data, treated as a bag of instances, is fed into a 2D convolutional neural network backbone, to capture the unique aging patterns in MRI. A dual graph attention aggregator is then proposed to learn the backbone features by exploiting the intra- and inter-instance relationships. Furthermore, a disentanglement branch is introduced to separate age-related features from age-independent structural representations to ameliorate the interference of redundant information on age prediction. To verify the effectiveness of the proposed framework, we evaluate it on two datasets, UK Biobank and ADNI, containing a total of 35,388 healthy individuals. Our proposed model demonstrates exceptional accuracy in estimating brain age, achieving a remarkable mean absolute error of 2.12 years in the UK Biobank. The results establish our approach as state-of-the-art compared to other competing brain age estimation models. In addition, the instance contribution scores identify the varied importance of brain areas for aging prediction, which provides deeper insights into the understanding of brain aging.

翻译：深度学习技术通过分析健康个体的磁共振成像（MRI）数据，在准确估计脑年龄方面展现出巨大潜力。然而，当前的脑年龄估计方法通常直接利用完整输入图像，忽视了两个重要因素：1）脑老化的异质性特征，即不同脑区域可能以不同速率退化；2）脑结构中存在与年龄无关的冗余信息。为克服这些局限，我们提出了一种基于双图注意力的解耦多实例学习（DGA-DMIL）框架用于改进脑年龄估计。具体而言，将三维MRI数据视为实例包，输入至二维卷积神经网络骨干中，以捕获MRI中独特的衰老模式。随后提出双图注意力聚合器，通过利用实例内与实例间的关系来学习骨干特征。此外，引入解耦分支，将年龄相关特征与年龄无关的结构表征分离，以减轻冗余信息对年龄预测的干扰。为验证所提框架的有效性，我们在包含总计35,388名健康个体的UK Biobank和ADNI两个数据集上进行了评估。我们的模型在脑年龄估计中展现出卓越准确性，在UK Biobank上实现了2.12年的惊人平均绝对误差。相较于其他竞争性脑年龄估计模型，该结果确立了本方法的最优性能。此外，实例贡献分数揭示了不同脑区域对衰老预测的差异化重要性，为理解脑老化提供了更深入的见解。