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.

翻译：深度学习的可解释性不足限制了其在临床实践中的应用。尽管已有方法可提升模型的可解释性，但这些方法主要基于显著性且专为分类任务设计，而医学影像中许多重要任务均为连续回归问题。为此，本文提出ExPeRT：一种专为回归任务设计的可解释原型模型。该模型通过计算潜在空间中样本与一组学习到的原型之间的距离，利用原型标签的加权均值进行样本预测。潜在空间中的距离经过正则化处理使其与标签差异相关，且每个原型均可可视化为训练集中的样本。图像级距离进一步由图像块级距离构建，其中两幅图像的图像块通过最优传输实现结构匹配。我们在两个图像数据集（成人磁共振与胎儿超声）上验证了所提模型在脑龄预测任务中的表现。该方法在取得最先进预测性能的同时，揭示了模型的推理过程。