mlsanityData to 7 Oct 2026

Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays

arXiv 2211.04279 ↗First posted 8 Nov 2022cs.CVcited by 20

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Abstract (arXiv)

The availability of large public datasets and the increased amount of computing power have shifted the interest of the medical community to high-performance algorithms. However, little attention is paid to the quality of the data and their annotations. High performance on benchmark datasets may be reported without considering possible shortcuts or artifacts in the data, besides, models are not tested on subpopulation groups. With this work, we aim to raise awareness about shortcuts problems. We validate previous findings, and present a case study on chest X-rays using two publicly available datasets. We share annotations for a subset of pneumothorax images with drains. We conclude with general recommendations for medical image classification.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 8 Nov 2022.

Builds on

13 of 28 references.

References · 13

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  4. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 459
  8. 2017On Fairness and Calibrationcited by 406
  9. 2019Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imagingcited by 215
  10. 2021Reading Race: AI Recognises Patient's Racial Identity In Medical Imagescited by 155
  11. 2020CheXclusion: Fairness gaps in deep chest X-ray classifierscited by 153
  12. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
1 more
  1. 2019Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classificationcited by 57

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 5; 2025: 4; 2026: 7.

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  1. 2023Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generationcited by 41
  2. 2024Shortcut Learning in Medical Image Segmentationcited by 16
  3. 2024Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methodscited by 14
  4. 2023How You Split Matters: Data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysiscited by 8
  5. 2023Augmenting Chest X-ray Datasets with Non-Expert Annotationscited by 6
  6. 2024Mask of truth: model sensitivity to unexpected regions of medical imagescited by 6
  7. 2024Copycats: the many lives of a publicly available medical imaging datasetcited by 5
  8. 2025Subgroup Performance Analysis in Hidden Stratificationscited by 3
  9. 2024Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learningcited by 2
  10. 2023Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitationscited by 1
  11. 2025Fairness and Robustness of CLIP-Based Models for Chest X-rayscited by 1
  12. 2026Dataset Diversity Metrics and Impact on Classification Modelscited by 1
8 more
  1. 2026Prevalence calibration as shortcut mitigationcited by 1
  2. 2025meval: A Statistical Toolbox for Fine-Grained Model Performance Analysiscited by 0
  3. 2025Localising Shortcut Learning in Pixel Space via Ordinal Scoring Correlations for Attribution Representations (OSCAR)cited by 0
  4. 2026Effect of Demographic Bias on Skin Lesion Classificationcited by 0
  5. 2026SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generationcited by 0
  6. 2026ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasetscited by 0
  7. 2026P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probingcited by 0
  8. 2026Look What the Probes Dragged In! Real-World Chest X-ray Shortcuts in MedCLIPcited by 0