Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks. However, conventional CNNs are not able to explain their reasoning process, therefore limiting their adoption in clinical practice. In this work, we propose an inherently interpretable CNN for regression using similarity-based comparisons (INSightR-Net) and demonstrate our methods on the task of diabetic retinopathy grading. A prototype layer incorporated into the architecture enables visualization of the areas in the image that are most similar to learned prototypes. The final prediction is then intuitively modeled as a mean of prototype labels, weighted by the similarities. We achieved competitive prediction performance with our INSightR-Net compared to a ResNet baseline, showing that it is not necessary to compromise performance for interpretability. Furthermore, we quantified the quality of our explanations using sparsity and diversity, two concepts considered important for a good explanation, and demonstrated the effect of several parameters on the latent space embeddings.

翻译：常规CNN无法解释其推理过程,因此限制了临床实践的采用。在这项工作中,我们提议使用基于相似性的比较(INSightR-Net),为回归提供内在可解释的CNN, 并展示我们关于糖尿病视网膜病分级任务的方法。 将原型层纳入建筑中,可以对图像中与所学原型最相近的区域进行直观化。 最终的预测随后被直观地模拟成一个原型标签的平均值,按相似点加权。我们实现了INSightR-Net与ResNet基准的竞争性预测性能,这表明没有必要在可解释性上损害性能。此外,我们用宽度和多样性来量化我们的解释性质量,这两个概念被认为对很好地解释非常重要,并展示了几个参数对潜伏空间的影响。