Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers decision-making. A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

翻译：深度学习分类器为基于光学相干断层扫描（OCT）及其血管成像（OCTA）的糖尿病视网膜病变（DR）自动诊断提供了最精确的方法。这些模型的部分优势源于其包含能够提供完成目标任务所需复杂性的隐藏层。然而，隐藏层也导致算法输出难以解释。本文提出一种基于生成对抗学习的新型生物标志物激活图（BAM）框架，使临床医生能够验证和理解分类器的决策过程。根据当前临床标准，包含456张黄斑扫描图像的数据集被分级为非转诊或转诊DR。用于评估BAM的DR分类器首先基于该数据集进行训练。BAM生成框架通过结合两个U型生成器设计，为该分类器提供有意义的可解释性。主生成器接受转诊扫描图像作为输入，并生成被分类器判定为非转诊的输出图像。BAM则通过主生成器输出与输入图像的差分图像构建。为确保BAM仅突出显示分类器利用的生物标志物，辅助生成器被训练执行相反操作：将非转诊扫描图像生成被分类器判定为转诊的扫描图像。生成的BAM突出显示了包括无灌注区和视网膜积液在内的已知病理特征。基于这些突出显示区域的全可解释分类器，可帮助临床医生更好地利用和验证自动化DR诊断。