  Radiologists usually observe anatomical regions of chest X-ray images as well
as the overall image before making a decision. However, most existing deep
learning models only look at the entire X-ray image for classification, failing
to utilize important anatomical information. In this paper, we propose a novel
multi-label chest X-ray classification model that accurately classifies the
image finding and also localizes the findings to their correct anatomical
regions. Specifically, our model consists of two modules, the detection module
and the anatomical dependency module. The latter utilizes graph convolutional
networks, which enable our model to learn not only the label dependency but
also the relationship between the anatomical regions in the chest X-ray. We
further utilize a method to efficiently create an adjacency matrix for the
anatomical regions using the correlation of the label across the different
regions. Detailed experiments and analysis of our results show the
effectiveness of our method when compared to the current state-of-the-art
multi-label chest X-ray image classification methods while also providing
accurate location information.
