Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Commercially available applications are detailed, and a comprehensive discussion of the current state of the art and potential future directions are provided.

翻译：近些年来,大量公开的胸前X光数据集的发布激发了研究兴趣,并增加了出版物的数量。在本文件中,我们利用对胸前射线的深入学习,审查所有研究报告,按任务对工作进行分类:图像水平预测(分类和回归)、分解、本地化、图像生成和域域调整。