  Chest X-ray images are commonly used in medical diagnosis, and AI models have
been developed to assist with the interpretation of these images. However, many
of these models rely on information from a single view of the X-ray, while
multiple views may be available. In this work, we propose a novel approach for
combining information from multiple views to improve the performance of X-ray
image classification. Our approach is based on the use of a convolutional
neural network to extract feature maps from each view, followed by an attention
mechanism implemented using a Vision Transformer. The resulting model is able
to perform multi-label classification on 41 labels and outperforms both
single-view models and traditional multi-view classification architectures. We
demonstrate the effectiveness of our approach through experiments on a dataset
of 363,000 X-ray images.
