论文标题
构造术：胸部X射线解释对胸部X射线照片的深度学习模型的概括
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
论文作者
论文摘要
使用智能手机拍摄胸部X射线照片是一种吸引人的解决方案，用于扩展深度学习模型的胸部X射线解释。但是，尚未对胸部X射线照片的胸部X射线算法进行性能。在这项研究中，当应用于胸部X射线照片时，我们测量了8种不同的胸部X射线模型的诊断性能。所有模型均由不同的组开发，并提交了Chexpert挑战，并重新应用于Chexphoto数据集中X射线的智能手机照片，而无需进行进一步调整。我们发现，将几种模型应用于胸部X射线的照片时的性能下降，但是即使有了这种下降，一些模型仍然对放射科医生的表现相当。可以进一步调查可以理解不同的模型训练程序如何影响对胸部X射线照片的模型概括。
The use of smartphones to take photographs of chest x-rays represents an appealing solution for scaled deployment of deep learning models for chest x-ray interpretation. However, the performance of chest x-ray algorithms on photos of chest x-rays has not been thoroughly investigated. In this study, we measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays. All models were developed by different groups and submitted to the CheXpert challenge, and re-applied to smartphone photos of x-rays in the CheXphoto dataset without further tuning. We found that several models had a drop in performance when applied to photos of chest x-rays, but even with this drop, some models still performed comparably to radiologists. Further investigation could be directed towards understanding how different model training procedures may affect model generalization to photos of chest x-rays.
