mlsanityData to 7 Oct 2026

Chest X-ray Image Classification: A Causal Perspective

arXiv 2305.12072 ↗First posted 20 May 2023eess.IV cs.CVcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 20 May 2023.

Builds on

13 of 21 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  5. 2021Deep Long-Tailed Learning: A Surveycited by 416
  6. 2021Causal Attention for Interpretable and Generalizable Graph Classificationcited by 108
  7. 2021On the Integration of Self-Attention and Convolutioncited by 74
  8. 2021CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretationcited by 57
  9. 2018Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networkscited by 33
  10. 2021Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosiscited by 24
  11. 2019ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rayscited by 22
  12. 2018SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Imagescited by 14
1 more
  1. 2020Deformable Gabor Feature Networks for Biomedical Image Classificationcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 4.

8 papers.

  1. 2025Structure Causal Models and LLMs Integration in Medical Visual Question Answeringcited by 6
  2. 2024CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarningcited by 2
  3. 2026Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQAcited by 1
  4. 2024Masked Latent Transformer with the Random Masking Ratio to Advance the Diagnosis of Dental Fluorosiscited by 0
  5. 2025MetaChest: Generalized few-shot learning of pathologies from chest X-rayscited by 0
  6. 2026Learning to Trim: End-to-End Causal Graph Pruning with Dynamic Anatomical Feature Banks for Medical VQAcited by 0
  7. 2026Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQAcited by 0
  8. 2026Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detectioncited by 0