Prototype Learning for Explainable Regression

The lack of explainability limits the adoption of deep learning models in clinical practice. While methods exist to improve the understanding of such models, these are mainly saliency-based and developed for classification, despite many important tasks in medical imaging being continuous regression problems. Therefore, in this work, we present ExPeRT: an explainable prototype-based model specifically designed for regression tasks. Our proposed model makes a sample prediction from the distances to a set of learned prototypes in latent space, using a weighted mean of prototype labels. The distances in latent space are regularized to be relative to label differences, and each of the prototypes can be visualized as a sample from the training set. The image-level distances are further constructed from patch-level distances, in which the patches of both images are structurally matched using optimal transport. We demonstrate our proposed model on the task of brain age prediction on two image datasets: adult MR and fetal ultrasound. Our approach achieved state-of-the-art prediction performance while providing insight in the model's reasoning process.