Reframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach

Neha Gianchandani, Mahsa Dibaji, Mariana Bento, Ethan MacDonald, Roberto Souza

Keywords: Brain Age Prediction, Interpretability, Voxel-level brain age, Grad-CAM

TL;DR: Voxel-wise age prediction model for brain age estimation improves interpretability and quantifiability compared to global prediction models using saliency maps.

Abstract: 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.

Submission Number: 88