论文标题
使用生成对抗网络（GAN）的数据增强，用于基于GAN的肺炎和COVID-19在胸部X射线图像中的检测
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
论文作者
论文摘要
成功培训卷积神经网络（CNN）需要大量数据。小型数据集网络概括不多。数据增强技术通过更有效地使用现有培训数据来改善神经网络的普遍性。但是，标准数据增强方法产生有限的合理替代数据。生成对抗网络（GAN）已被用于生成新数据并改善CNN的性能。然而，与CNN相比，培训剂的数据增强技术的探索不足。在这项工作中，我们提出了一种新的GAN架构，用于增强胸部X射线，以使用生成模型对肺炎和Covid-19的半监督检测。我们表明，所提出的GAN可用于有效地增强数据并提高肺炎和Covid-19胸部X射线疾病的分类准确性。我们在两个不同的X射线数据集上使用深卷积GAN和传统的增强方法（旋转，变焦等）进行比较，并显示我们的基于GAN的增强方法超过了其他增强方法，可以训练GAN在检测X射线图像中检测异常的GAN。
Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.
