  To enable a deep learning-based system to be used in the medical domain as a
computer-aided diagnosis system, it is essential to not only classify diseases
but also present the locations of the diseases. However, collecting
instance-level annotations for various thoracic diseases is expensive.
Therefore, weakly supervised localization methods have been proposed that use
only image-level annotation. While the previous methods presented the disease
location as the most discriminative part for classification, this causes a deep
network to localize wrong areas for indistinguishable X-ray images. To solve
this issue, we propose a spatial attention method using disease masks that
describe the areas where diseases mainly occur. We then apply the spatial
attention to find the precise disease area by highlighting the highest
probability of disease occurrence. Meanwhile, the various sizes, rotations and
noise in chest X-ray images make generating the disease masks challenging. To
reduce the variation among images, we employ an alignment module to transform
an input X-ray image into a generalized image. Through extensive experiments on
the NIH-Chest X-ray dataset with eight kinds of diseases, we show that the
proposed method results in superior localization performances compared to
state-of-the-art methods.
