Abstract
The chest X-ray (CXR) is a widely used and easily accessible medical test for diagnosing common chest diseases. Recently, there have been numerous advancements in deep learning-based methods capable of effectively classifying CXR. However, assessing whether these algorithms truly capture the cause-and-effect relationship between diseases and their underlying causes, or merely learn to map labels to images, remains a challenge. In this paper, we propose a causal approach to address the CXR classification problem, which involves constructing a structural causal model (SCM) and utilizing backdoor adjustment to select relevant visual information for CXR classification. Specifically, we design various probability optimization functions to eliminate the influence of confounding factors on the learning of genuine causality. Experimental results demonstrate that our proposed method surpasses the performance of two open-source datasets in terms of classification performance. To access the source code for our approach, please visit: https://github.com/zc2024/Causal_CXR.
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This work was supported by the National Natural Science Foundation of China (62272337).
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Nie, W., Zhang, C., Song, D., Bai, Y., Xie, K., Liu, AA. (2023). Chest X-ray Image Classification: A Causal Perspective. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_3
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