Chest radiography is a relatively cheap, widely available medical procedure that conveys key information for making diagnostic decisions. Chest X-rays are almost always used in the diagnosis of respiratory diseases such as pneumonia or the recent COVID-19. In this paper, we propose a self-supervised deep neural network that is pretrained on an unlabeled chest X-ray dataset. The learned representations are transferred to downstream task - the classification of respiratory diseases. The results obtained on four public datasets show that our approach yields competitive results without requiring large amounts of labeled training data.
翻译:切片放射是一种相对廉价的、可广泛获取的医疗程序,可以传递关键信息作出诊断决定。切片X光片几乎总是用于诊断肺炎或最近的COVID-19等呼吸系统疾病。在本文件中,我们提议建立一个自我监督的深神经网络,先用未贴标签的胸腔X光数据集进行训练。所学的表述被转移到下游任务——呼吸系统疾病分类。四个公共数据集的结果显示,我们的方法产生竞争性的结果,而不需要大量标记的培训数据。