论文标题
量化胸部X射线深度学习中横向视图的价值
Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays
论文作者
论文摘要
胸部X射线预测中的大多数深度学习模型由于缺乏可用的视图而采用后翅目（PA）视图。 Padchest是一个大规模的胸部X射线数据集，具有近200个标签和多个可用视图。在这项工作中，我们使用Padchest探索了多种方法，以合并PA和横向视图，以预测与X射线图像相关的放射线标签。我们发现合并模型的不同方法以不同的方式利用横向视图。我们还发现，包括横向视图可以提高数据集中32个标签的性能，同时对其他标签进行中性。总体表现的提高与仅使用PA视图与训练集中患者的两倍相当。
Most deep learning models in chest X-ray prediction utilize the posteroanterior (PA) view due to the lack of other views available. PadChest is a large-scale chest X-ray dataset that has almost 200 labels and multiple views available. In this work, we use PadChest to explore multiple approaches to merging the PA and lateral views for predicting the radiological labels associated with the X-ray image. We find that different methods of merging the model utilize the lateral view differently. We also find that including the lateral view increases performance for 32 labels in the dataset, while being neutral for the others. The increase in overall performance is comparable to the one obtained by using only the PA view with twice the amount of patients in the training set.
