  Chest X-rays are the most commonly available and affordable radiological
examination for screening thoracic diseases. According to the domain knowledge
of screening chest X-rays, the pathological information usually lay on the lung
and heart regions. However, it is costly to acquire region-level annotation in
practice, and model training mainly relies on image-level class labels in a
weakly supervised manner, which is highly challenging for computer-aided chest
X-ray screening. To address this issue, some methods have been proposed
recently to identify local regions containing pathological information, which
is vital for thoracic disease classification. Inspired by this, we propose a
novel deep learning framework to explore discriminative information from lung
and heart regions. We design a feature extractor equipped with a multi-scale
attention module to learn global attention maps from global images. To exploit
disease-specific cues effectively, we locate lung and heart regions containing
pathological information by a well-trained pixel-wise segmentation model to
generate binarization masks. By introducing element-wise logical AND operator
on the learned global attention maps and the binarization masks, we obtain
local attention maps in which pixels are $1$ for lung and heart region and $0$
for other regions. By zeroing features of non-lung and heart regions in
attention maps, we can effectively exploit their disease-specific cues in lung
and heart regions. Compared to existing methods fusing global and local
features, we adopt feature weighting to avoid weakening visual cues unique to
lung and heart regions. Evaluated by the benchmark split on the publicly
available chest X-ray14 dataset, the comprehensive experiments show that our
method achieves superior performance compared to the state-of-the-art methods.
