Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification.

TLDR: A three-branch attention guided convolution neural network (AG-CNN) that learns from disease-specific regions to avoid noise and improve alignment, and also integrates a global branch to compensate the lost discriminative cues by local branch.

Abstract: This paper considers the task of thorax disease classification on chest X-ray images Existing methods generally use the global image as input for network learning Such a strategy is limited in two aspects 1) A thorax disease usually happens in (small) localized areas which are disease specific Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN) AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch Specifically, we first learn a global CNN branch using global images Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image The local region is used for training a local CNN branch Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch The Comprehensive experiment is conducted on the ChestX-ray14 dataset We first report a strong global baseline producing an average AUC of 0841 with ResNet-50 as backbone After combining the local cues with the global information, AG-CNN improves the average AUC to 0868 While DenseNet-121 is used, the average AUC achieves 0871, which is a new state of the art in the community