Missed diagnosis has been a serious public health issue in clinical diagnosis and treatment, it may cause disease deterioration and reduce cure rate. In recent years, many deep learning approaches have been proposed for automated medical image classification. In this study, we propose a new methodology to detect the images missed diagnosed by deep learning classifiers. Based on the intermediate feature maps of deep learning classifiers, the proposed model can detect missed diagnosed sample and reduce the missed diagnoses rate, with no obvious decrease in the accuracy. The proposed model is constructed using generative adversarial networks and autoencoders to learn consistent mapping from data space to latent space, and is trained with adversarial examples. After training, the output of the discriminator is used to recognize missed diagnosed samples. The method is evaluated on different network architectures and various types of medical image datasets and achieves promising results. Compared with other state-of-the-art approaches, the proposed method shows superior performance on most datasets.

在临床诊断和治疗中，漏诊一直是严重的公共卫生问题，它可能导致疾病恶化并降低治愈率。近年来，已经提出了许多用于自动医学图像分类的深度学习方法。在这项研究中，我们提出了一种新的方法来检测深度学习分类器所漏诊的图像。基于深度学习分类器的中间特征图，该模型可以检测出漏诊的样本，降低漏诊率，准确性没有明显下降。利用生成的对抗网络和自动编码器构造该模型，以学习从数据空间到潜在空间的一致映射，并通过对抗性示例进行训练。训练结束后，鉴别器的输出用于识别错过的诊断样本。该方法在不同的网络体系结构和各种类型的医学图像数据集上进行了评估，并取得了可喜的结果。与其他最新方法相比，该方法在大多数数据集上均表现出了优异的性能。