Multi-label local awareness and global co-occurrence priori learning improve chest X-ray classification

When analyzing screening chest X-ray (CXR) images, radiologists can naturally consider information about the relationships between pathologies, namely pathological co-occurrence, and interdependence. Such topologically meaningful pathological relationships provide complementary a priori knowledge and can improve the radiologist’s classification accuracy. Unfortunately, most existing deep learning systems focus only on regression from input to binary labels, lack the ability to jointly analyze and integrate global and local information from these multiple labels, and the ability to exploit this valuable prior knowledge of topology. We suppose that the algorithm will distinguish co-occurring pathologies easily if it is guided to learn potentially valuable information between multiple pathologies. We propose a novel multi-label global–local analysis method named CXR \(\times\) $\times$ MLAGCPL for disease recognition in CXR images. We first designed two methods for constructing graphs: building adaptive local graph on a single image and creating global co-occurrence graph in a data-driven manner. Then two modules, local awareness module (LAM) and global co-occurrence priori learning module (GCPL), were designed to jointly learn local class correlation and global inter-class dependency patterns. We have evaluated the effectiveness of the proposed CXR \(\times\) $\times$ MLAGCPL in two large-scale CXR datasets (ChestX-Ray14 and CheXpert). Our model achieved state-of-the-art performance: the mean AUC scores for the 14 pathologies were 0.828 and 0.813, respectively. The proposed LAM and GCPL can jointly effectively improve the recognition performance. Our model is accurate in terms of classification accuracy and generalization due to competing approaches.