Multi-attention Network for Thoracic Disease Classification and Localization

Abstract

The chest X-ray is one of the most commonly available radiological examinations for diagnosing lung diseases. This task remains a major challenge due to 1) the shortage of accurate annotations for chest X-ray examinations, 2) the diversity of lesion areas on X-rays from different thoracic disease and 3) the problem of class imbalance in existing chest X-ray databases. In this paper, we propose a new multi-attention convolutional neural network for thoracic disease classification and localization. First, the framework is equipped with squeeze-and-excitation (SE) block as a feature attention module to offer a chance of cross-channel feature recalibration. Second, we propose a novel space attention module to combine global and local information. Third, we present a hard examples attention module to alleviate the class imbalance problem. The comprehensive experiments are performed on the ChestX-ray14 dataset. Quantitative and qualitative results demonstrate that our method outperforms the state-of-the-art algorithm.