Reframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach
This work addresses interpretability issues in brain health biomarker estimation for medical imaging researchers, but it is incremental as it builds on existing deep learning methods.
The authors tackled the problem of interpreting brain age predictions from MR images by reframing it as a voxel-wise regression task, resulting in models that provide spatial and quantitative insights into brain aging, though no concrete numerical improvements were reported.
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.