  Identifying and locating diseases in chest X-rays are very challenging, due
to the low visual contrast between normal and abnormal regions, and distortions
caused by other overlapping tissues. An interesting phenomenon is that there
exist many similar structures in the left and right parts of the chest, such as
ribs, lung fields and bronchial tubes. This kind of similarities can be used to
identify diseases in chest X-rays, according to the experience of
broad-certificated radiologists. Aimed at improving the performance of existing
detection methods, we propose a deep end-to-end module to exploit the
contralateral context information for enhancing feature representations of
disease proposals. First of all, under the guidance of the spine line, the
spatial transformer network is employed to extract local contralateral patches,
which can provide valuable context information for disease proposals. Then, we
build up a specific module, based on both additive and subtractive operations,
to fuse the features of the disease proposal and the contralateral patch. Our
method can be integrated into both fully and weakly supervised disease
detection frameworks. It achieves 33.17 AP50 on a carefully annotated private
chest X-ray dataset which contains 31,000 images. Experiments on the NIH chest
X-ray dataset indicate that our method achieves state-of-the-art performance in
weakly-supervised disease localization.
