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Radiologists usually observe anatomical regions of chest X-ray images as wellas the overall image before making a decision. However, most existing deeplearning models only look at the entire X-ray image for classification, failingto utilize important anatomical information. In this paper, we propose a novelmulti-label chest X-ray classification model that accurately classifies theimage finding and also localizes the findings to their correct anatomicalregions. Specifically, our model consists of two modules, the detection moduleand the anatomical dependency module. The latter utilizes graph convolutionalnetworks, which enable our model to learn not only the label dependency butalso the relationship between the anatomical regions in the chest X-ray. Wefurther utilize a method to efficiently create an adjacency matrix for theanatomical regions using the correlation of the label across the differentregions. Detailed experiments and analysis of our results show theeffectiveness of our method when compared to the current state-of-the-artmulti-label chest X-ray image classification methods while also providingaccurate location information.

AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray