  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 0.841 with
ResNet-50 as backbone. After combining the local cues with the global
information, AG-CNN improves the average AUC to 0.868. While DenseNet-121 is
used, the average AUC achieves 0.871, which is a new state of the art in the
community.
