  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.
