Lesion Location Attention Guided Network for Multi-Label Thoracic Disease Classification in Chest X-Rays.

Traditional clinical experiences have shown the benefit of lesion location attention for improving clinical diagnosis tasks. Inspired by this point of interest, in this paper we propose a novel lesion location attention guided network named LLAGnet to focus on the discriminative features from lesion locations for multi-label thoracic disease classification in chest X-rays (CXRs). By revealing the equivalence of the region-level attention (RLA) and channel-level attention (CLA), we find that the RLA is available as priors for object localization while the CLA implicitly provides high weights to the attractive channels, which both enable lesion location attention excitation. To integrate the advantages from both mechanisms, the proposed LLAGnet is structured with two corresponding attention modules, i.e., the RLA and CLA modules. Specifically, the RLA module consists of the global and local branches. And the weakly supervised attention mechanism embedded in the global branch can obtain visual regions of lesion locations by back-propagating gradients. Then the optimal attention region is amplified and applied to the local branch to provide more fine-grained features for the image classification. Finally, the CLA module adaptively enhances the weights of channel-wise features from the lesion locations by modeling interdependencies among channels. Extensive experiments on the ChestX-ray14 dataset clearly substantiate the effectiveness of LLAGnet as compared with the state-of-the-art baselines.