论文标题
使用未配对的图像到图像翻译的COVID-19胸部X射线合成
Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation
论文作者
论文摘要
由于缺乏冠状病毒疾病阳性患者的胸部X光片（COVID-19）的胸部X光片的动机，我们通过使用不受欢迎的域适应方法来构建合成covid-19胸部X射线X射线X射线图像的首个开放式数据集，该图像通过进行类别的条件和辅助培训。我们的贡献是双重的。首先，在使用合成图像作为其他训练集时，我们使用各种深度学习体系结构对Covid-19检测进行了可观的性能改进。其次，我们展示了如何通过仅在合成数据上培训的可比较检测性能，可以通过实现可比的检测性能来充当数据匿名工具。此外，提议的数据生成框架特别是COVID-19检测提供了可行的解决方案，通常为医疗图像分类任务提供了可行的解决方案。我们公开可用的基准数据集由21,295个合成COVID-19胸X射线图像组成。该数据集收集的见解可用于与19日大流行的战斗中的预防作用。
Motivated by the lack of publicly available datasets of chest radiographs of positive patients with Coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training. Our contributions are twofold. First, we show considerable performance improvements on COVID-19 detection using various deep learning architectures when employing synthetic images as additional training set. Second, we show how our image synthesis method can serve as a data anonymization tool by achieving comparable detection performance when trained only on synthetic data. In addition, the proposed data generation framework offers a viable solution to the COVID-19 detection in particular, and to medical image classification tasks in general. Our publicly available benchmark dataset consists of 21,295 synthetic COVID-19 chest X-ray images. The insights gleaned from this dataset can be used for preventive actions in the fight against the COVID-19 pandemic.
