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.

通过分析磁共振成像（MRI）数据，利用深度学习技术准确估计大脑年龄是有潜力的，但现有方法往往直接利用整个输入图像，忽视了两个重要考虑因素：1）大脑老化的异质性，即不同脑区可能以不同速率退化，和 2）大脑结构中存在与年龄无关的冗余信息。为了克服这些局限性，我们提出了一种基于双图注意的解耦多实例学习（DGA-DMIL）框架以改善大脑年龄估计。我们通过将 3D MRI 数据视为一组实例，输入到 2D 卷积神经网络主干中，以捕捉 MRI 中独特的老化模式。然后，提出了一个双图注意聚合器，通过利用实例内和实例间的关系学习主干特征。此外，我们还引入了一个解耦分支，以将与年龄相关的特征与与年龄无关的结构表示分离，以改善冗余信息对年龄预测的干扰。为了验证所提出的框架的有效性，我们对包含 35,388 名健康个体的 UK Biobank 和 ADNI 两个数据集进行评估。我们的模型在 UK Biobank 中显示出令人瞩目的准确性，达到了 2.12 年的平均绝对误差。与其他竞争的大脑年龄估计模型相比，我们的方法已成为最先进的。此外，实例贡献分数在年龄预测中识别了不同脑区的重要性，为对大脑老化的理解提供了更深入的洞察。