The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels. This information is critical for building trust in models and finding their biases. Until now, automated deep learning visualization solutions have identified regions of images used by classifiers, but these solutions are too coarse, too noisy, or have a limited representation of the way images can change. We propose a novel method for formulating and presenting spatial explanations of disease evidence, called deformation field interpretation with generative adversarial networks (DeFI-GAN). An adversarially trained generator produces deformation fields that modify images of diseased patients to resemble images of healthy patients. We validate the method studying chronic obstructive pulmonary disease (COPD) evidence in chest x-rays (CXRs) and Alzheimer's disease (AD) evidence in brain MRIs. When extracting disease evidence in longitudinal data, we show compelling results against a baseline producing difference maps. DeFI-GAN also highlights disease biomarkers not found by previous methods and potential biases that may help in investigations of the dataset and of the adopted learning methods.

翻译：深度学习模型的高度复杂性导致难以解释其识别哪些证据与特定疾病标签相关。这些信息对于建立对模型的信任以及发现其偏差至关重要。迄今为止，自动化深度学习可视化方案虽能识别分类器关注的图像区域，但这些方案过于粗糙、噪声过大，或对图像变化方式的表征能力有限。我们提出一种新颖的疾病证据空间解释构建与呈现方法——基于生成对抗网络的变形场解释（DeFI-GAN）。通过对抗训练生成的生成器可产生变形场，将患病患者的图像修改为类似健康患者的图像。我们通过研究胸部X光片（CXR）中的慢性阻塞性肺疾病（COPD）证据以及脑部MRI中的阿尔茨海默病（AD）证据验证了该方法。在纵向数据中提取疾病证据时，我们展示了相较于生成差异图的基线方法的显著优势。DeFI-GAN还揭示了先前方法未发现的疾病生物标志物及潜在偏差，有助于对数据集及所采用学习方法展开深入研究。