  Locating lesions is important in the computer-aided diagnosis of X-ray
images. However, box-level annotation is time-consuming and laborious. How to
locate lesions accurately with few, or even without careful annotations is an
urgent problem. Although several works have approached this problem with
weakly-supervised methods, the performance needs to be improved. One obstacle
is that general weakly-supervised methods have failed to consider the
characteristics of X-ray images, such as the highly-structural attribute. We
therefore propose the Cross-chest Graph (CCG), which improves the performance
of automatic lesion detection by imitating doctor's training and
decision-making process. CCG models the intra-image relationship between
different anatomical areas by leveraging the structural information to simulate
the doctor's habit of observing different areas. Meanwhile, the relationship
between any pair of images is modeled by a knowledge-reasoning module to
simulate the doctor's habit of comparing multiple images. We integrate
intra-image and inter-image information into a unified end-to-end framework.
Experimental results on the NIH Chest-14 database (112,120 frontal-view X-ray
images with 14 diseases) demonstrate that the proposed method achieves
state-of-the-art performance in weakly-supervised localization of lesions by
absorbing professional knowledge in the medical field.
