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:深层学习分类提供基于光学一致性成像仪(OCT)及其血管成像仪(OCTA)的自动诊断糖尿病视网膜病(DR)的最精确手段。这些模型的力量部分归因于包含隐藏的层层,这些层提供了完成预期任务所需的复杂性。然而,隐藏层也使算法输出难以解释。在这里,我们引入了一个基于基因化对立学习的新颖的生物标志激活图(BAM)框架,使临床医生能够核查和理解分类师的决策。包括456个剖面扫描的数据集,根据目前的临床标准,被评为不可调取或可引用的解析解的解析流体。用于评估我们BAM的DRGL,首先根据这个数据集对用于评估我们BAM的隐藏层进行了培训。BAM生成框架是通过对两个U型发电机进行梳理,为这个分类师提供了有意义的解释。主机的扫描器可以将可参考的扫描结果归类为不可调解析的输出。然后,根据目前的临床标准,BAM将作为不可调和可调和可调的解的解析的解析的解析的解的解的解析点图像图像进行分解。BAM将只能制成为用于制作一个更精确的图像,以生成的图像,以生成的升级为制成一个更精确的A。