Abstract
Neural networks are proven to be remarkably successful for classification and diagnosis in medical applications. However, the ambiguity in the decision-making process and the interpretability of the learned features is a matter of concern. In this work, we propose a method for improving the feature interpretability of neural network classifiers. Initially, we propose a baseline convolutional neural network with state of the art performance in terms of accuracy and weakly supervised localization. Subsequently, the loss is modified to integrate robustness to adversarial examples into the training process. In this work, feature interpretability is quantified via evaluating the weakly supervised localization using the ground truth bounding boxes. Interpretability is also visually assessed using class activation maps and saliency maps. The method is applied to NIH ChestX-ray14, the largest publicly available chest x-rays dataset. We demonstrate that the adversarially robust optimization paradigm improves feature interpretability both quantitatively and visually.
Abstract (translated)
神经网络在医学分类和诊断中被证明是非常成功的。然而，决策过程中的模糊性和所学特征的可解释性是一个值得关注的问题。本文提出了一种提高神经网络分类器特征解释能力的方法。首先，我们提出了一个基线卷积神经网络，在准确性和弱监督定位方面具有最先进的性能。随后，对损失进行了修改，以将对抗性示例的鲁棒性整合到培训过程中。在这项工作中，通过使用地面真值边界框评估弱监督定位来量化特征的可解释性。还可以使用类激活图和显著性图直观地评估可解释性。该方法适用于NIH ChestX-Ray14，这是最大的公共胸部X光数据集。我们证明了具有对抗性的鲁棒优化范式在数量和视觉上都提高了特征的可解释性。
URL
https://arxiv.org/abs/1905.03767
