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

arXiv: https://arxiv.org/abs/2211.04279 · HTML: https://mlsanity.com/paper/2211.04279/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 8 Nov 2022.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging](https://mlsanity.com/paper/1909.12475/) (2019)
- [Reading Race: AI Recognises Patient's Racial Identity In Medical Images](https://mlsanity.com/paper/2107.10356/) (2021)
- [CheXclusion: Fairness gaps in deep chest X-ray classifiers](https://mlsanity.com/paper/2003.00827/) (2020)
- [Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees](https://mlsanity.com/paper/1806.06055/) (2018)
- [Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification](https://mlsanity.com/paper/1909.01940/) (2019)

## Built on by (most cited first, 20 of 20)

- [Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation](https://mlsanity.com/paper/2312.14223/) (2023)
- [Shortcut Learning in Medical Image Segmentation](https://mlsanity.com/paper/2403.06748/) (2024)
- [Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods](https://mlsanity.com/paper/2406.12142/) (2024)
- [How You Split Matters: Data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysis](https://mlsanity.com/paper/2309.00350/) (2023)
- [Augmenting Chest X-ray Datasets with Non-Expert Annotations](https://mlsanity.com/paper/2309.02244/) (2023)
- [Copycats: the many lives of a publicly available medical imaging dataset](https://mlsanity.com/paper/2402.06353/) (2024)
- [Mask of truth: model sensitivity to unexpected regions of medical images](https://mlsanity.com/paper/2412.04030/) (2024)
- [Subgroup Performance Analysis in Hidden Stratifications](https://mlsanity.com/paper/2503.10382/) (2025)
- [Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning](https://mlsanity.com/paper/2407.17543/) (2024)
- [Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations](https://mlsanity.com/paper/2308.06434/) (2023)
- [Fairness and Robustness of CLIP-Based Models for Chest X-rays](https://mlsanity.com/paper/2507.21291/) (2025)
- [Localising Shortcut Learning in Pixel Space via Ordinal Scoring Correlations for Attribution Representations (OSCAR)](https://mlsanity.com/paper/2512.18888/) (2025)
- [Dataset Diversity Metrics and Impact on Classification Models](https://mlsanity.com/paper/2603.15276/) (2026)
- [Prevalence calibration as shortcut mitigation](https://mlsanity.com/paper/2609.07922/) (2026)
- [meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis](https://mlsanity.com/paper/2512.17409/) (2025)
- [Effect of Demographic Bias on Skin Lesion Classification](https://mlsanity.com/paper/2606.03214/) (2026)
- [SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation](https://mlsanity.com/paper/2606.30201/) (2026)
- [ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets](https://mlsanity.com/paper/2607.06889/) (2026)
- [P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing](https://mlsanity.com/paper/2608.10131/) (2026)
- [Look What the Probes Dragged In! Real-World Chest X-ray Shortcuts in MedCLIP](https://mlsanity.com/paper/2608.12086/) (2026)

## 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.