论文标题
Chexphoto：10,000多个胸部X射线的照片和转换，用于深度学习鲁棒性
CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
论文作者
论文摘要
深度学习算法用于胸部X射线解释的临床部署需要一种解决方案，该解决方案可以集成到世界各地的临床工作流程中。扩展部署的一种吸引人的方法是通过捕获X射线照片来利用智能手机的普遍存在，以使用WhatsApp等消息服务与临床医生共享。但是，将胸部X射线算法应用于胸部X射线的照片需要可靠的分类，而在存在用于训练机器学习模型的数字X射线中通常不会遇到文物。我们介绍了Chexphoto，这是一个智能手机照片的数据集以及从Chexpert数据集采样的胸部X射线的合成照片转换。为了生成Chexphoto，我们（1）在不同的设置下自动并手动捕获数字X射线的照片，以及（2）针对目标的数字X射线的合成转换，使它们看起来像数字X射线和X射线胶片的照片。我们将此数据集发布为用于测试和改善深度学习算法的鲁棒性的资源，以在胸部X射线智能手机照片上自动化胸部X射线解释。
Clinical deployment of deep learning algorithms for chest x-ray interpretation requires a solution that can integrate into the vast spectrum of clinical workflows across the world. An appealing approach to scaled deployment is to leverage the ubiquity of smartphones by capturing photos of x-rays to share with clinicians using messaging services like WhatsApp. However, the application of chest x-ray algorithms to photos of chest x-rays requires reliable classification in the presence of artifacts not typically encountered in digital x-rays used to train machine learning models. We introduce CheXphoto, a dataset of smartphone photos and synthetic photographic transformations of chest x-rays sampled from the CheXpert dataset. To generate CheXphoto we (1) automatically and manually captured photos of digital x-rays under different settings, and (2) generated synthetic transformations of digital x-rays targeted to make them look like photos of digital x-rays and x-ray films. We release this dataset as a resource for testing and improving the robustness of deep learning algorithms for automated chest x-ray interpretation on smartphone photos of chest x-rays.
