
Chest X-ray (CXR) is one of the most popular and important imaging examination methods for screening, diagnosing, and managing public health. 
%
However, the clinical interpretation of a CXR image requires the expertise of highly qualified radiologists whose diagnosis may include potential biases. 
%
Firstly, the geographical bias where some diseases appear more frequently in specific areas but are very rare in some others. 
%
Secondly, expertise bias where radiologists are only good at diagnosing a specific set of diseases. 
%
Thirdly, inconsistency among radiologists, especially on ambiguous edge cases, causes more noisy labeled data.
%
The automated CXR interpretation system that assists radiologists in decision-making would, therefore, tackle these problems.

An automated CXR interpretation software, at the level of an experienced radiologist, could provide a great benefit in both consistency and efficiency of diagnosis. 
%
However, it poses a computational challenge to develop software that matches the expertise and experiences of practicing radiologists. 
%
Taking recent advantage of Artificial Intelligent (AI) and Deep Learning (DL), many systems can surpass the human-level decision in terms of accuracy in a number of computer vision tasks. 
%
However, DL, in particular supervised learning, generally requires large-scale and high-quality labeled datasets to achieve the human level of accuracy. 
%
Most importantly, these datasets are not easy to obtain in practice. 
%
This is due to the expertise required to label a large amount of data, and doctors' consensus cannot be reached easily. 
%
Thus, acquiring a high-quality and large labeled dataset is costly and time-consuming.

