论文标题
Indightr-NET：可解释的神经网络，用于使用基于相似性的原型示例的相似性比较来回归的神经网络
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples
论文作者
论文摘要
卷积神经网络（CNN）在一系列医学成像任务中表现出了出色的性能。但是，常规的CNN无法解释其推理过程，因此限制了它们在临床实践中的采用。在这项工作中，我们建议使用基于相似性的比较（Indightr-net）进行回归的固有解释的CNN，并演示了我们关于糖尿病性视网膜病的任务的方法。结合到体系结构中的原型层可以可视化图像中最相似的图像区域。然后将最终预测直观地建模为原型标签的平均值，并由相似性加权。与Resnet基线相比，我们通过无效的网络实现了竞争性预测性能，这表明没有必要损害性能以实现可解释性。此外，我们使用稀疏性和多样性量化了解释的质量，这两个概念对于良好的解释很重要，并证明了几个参数对潜在空间嵌入的影响。
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.
