  The chest X-ray (CXR) is one of the most common and easy-to-get medical tests
used to diagnose common diseases of the chest. Recently, many deep
learning-based methods have been proposed that are capable of effectively
classifying CXRs. Even though these techniques have worked quite well, it is
difficult to establish whether what these algorithms actually learn is the
cause-and-effect link between diseases and their causes or just how to map
labels to photos.In this paper, we propose a causal approach to address the CXR
classification problem, which constructs a structural causal model (SCM) and
uses the backdoor adjustment to select effective visual information for CXR
classification. Specially, we design different probability optimization
functions to eliminate the influence of confounders on the learning of real
causality. Experimental results demonstrate that our proposed method
outperforms the open-source NIH ChestX-ray14 in terms of classification
performance.
