  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.
