# Chest X-ray Image Classification: A Causal Perspective

arXiv: https://arxiv.org/abs/2305.12072 · HTML: https://mlsanity.com/paper/2305.12072/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 20 May 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Causal Attention for Interpretable and Generalizable Graph Classification](https://mlsanity.com/paper/2112.15089/) (2021)
- [On the Integration of Self-Attention and Convolution](https://mlsanity.com/paper/2111.14556/) (2021)
- [CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation](https://mlsanity.com/paper/2101.06871/) (2021)
- [Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks](https://mlsanity.com/paper/1803.04565/) (2018)
- [Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis](https://mlsanity.com/paper/2112.12349/) (2021)
- [ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays](https://mlsanity.com/paper/1904.00325/) (2019)
- [SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images](https://mlsanity.com/paper/1810.12959/) (2018)
- [Deformable Gabor Feature Networks for Biomedical Image Classification](https://mlsanity.com/paper/2012.04109/) (2020)

## Built on by (most cited first, 8 of 8)

- [Structure Causal Models and LLMs Integration in Medical Visual Question Answering](https://mlsanity.com/paper/2505.02703/) (2025)
- [CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning](https://mlsanity.com/paper/2408.04949/) (2024)
- [Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA](https://mlsanity.com/paper/2604.20306/) (2026)
- [Masked Latent Transformer with the Random Masking Ratio to Advance the Diagnosis of Dental Fluorosis](https://mlsanity.com/paper/2404.13564/) (2024)
- [MetaChest: Generalized few-shot learning of pathologies from chest X-rays](https://mlsanity.com/paper/2509.25590/) (2025)
- [Learning to Trim: End-to-End Causal Graph Pruning with Dynamic Anatomical Feature Banks for Medical VQA](https://mlsanity.com/paper/2603.26028/) (2026)
- [Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA](https://mlsanity.com/paper/2606.01044/) (2026)
- [Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection](https://mlsanity.com/paper/2609.10988/) (2026)

## Abstract (arXiv)

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.