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.

中文翻译：

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自监督深度卷积神经网络用于胸部X射线分类

胸部放射线照相术是一种相对便宜的，可广泛使用的医疗程序，可传达做出诊断决定的关键信息。胸部X光几乎总是用于诊断呼吸系统疾病，例如肺炎或最近的COVID-19。在本文中，我们提出了一种自我监督的深度神经网络，该网络在未标记的胸部X射线数据集上进行了预训练。学到的表示将转移到下游任务-呼吸系统疾病的分类。在四个公共数据集上获得的结果表明，我们的方法无需大量标记培训数据即可产生竞争性结果。