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.

本文用因果学方法构建了结构性因果模型(SCM)，利用反向门控调整选取肺部X光的有效视觉信息，去除混杂物对真实因果性学习的影响，实验结果表明，我们所提出的方法能够胜过开源NIH ChestX-ray14在分类性能方面。

胸部 X 射线图像分类：因果关系视角