Deep learning models have achieved state-of-the-art results in estimating
brain age, which is an important brain health biomarker, from magnetic
resonance (MR) images. However, most of these models only provide a global age
prediction, and rely on techniques, such as saliency maps to interpret their
results. These saliency maps highlight regions in the input image that were
significant for the model's predictions, but they are hard to be interpreted,
and saliency map values are not directly comparable across different samples.
In this work, we reframe the age prediction problem from MR images to an
image-to-image regression problem where we estimate the brain age for each
brain voxel in MR images. We compare voxel-wise age prediction models against
global age prediction models and their corresponding saliency maps. The results
indicate that voxel-wise age prediction models are more interpretable, since
they provide spatial information about the brain aging process, and they
benefit from being quantitative.

深度学习模型通过磁共振成像在估计大脑年龄方面取得了最先进的结果。我们将年龄预测问题从全局预测转化为图像回归问题，比较了基于体素的年龄预测模型与基于全局的模型以及它们对应的显著性地图。结果表明，基于体素的年龄预测模型更具解释性，因为它们提供了关于大脑衰老过程的空间信息，并从定量化中受益。