Reframing the Brain Age Prediction Problem to a More Interpretable and
  Quantitative Approach
Neha Gianchandani Mahsa Dibaji Mariana Bento Ethan MacDonald Roberto Souza
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.