# Structured dataset documentation: a datasheet for CheXpert

arXiv: https://arxiv.org/abs/2105.03020 · HTML: https://mlsanity.com/paper/2105.03020/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 7 of 7)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification](https://mlsanity.com/paper/1909.01940/) (2019)
- [VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels](https://mlsanity.com/paper/2102.11467/) (2021)
- [A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging](https://mlsanity.com/paper/1904.01638/) (2019)

## Built on by (most cited first, 6 of 6)

- [In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review](https://mlsanity.com/paper/2501.10727/) (2025)
- [Copycats: the many lives of a publicly available medical imaging dataset](https://mlsanity.com/paper/2402.06353/) (2024)
- [Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal](https://mlsanity.com/paper/2306.04502/) (2023)
- [ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging](https://mlsanity.com/paper/2305.15617/) (2023)
- [Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare](https://mlsanity.com/paper/2204.13194/) (2022)
- [SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction](https://mlsanity.com/paper/2307.08003/) (2023)

## Abstract (arXiv)

Billions of X-ray images are taken worldwide each year. Machine learning, and deep learning in particular, has shown potential to help radiologists triage and diagnose images. However, deep learning requires large datasets with reliable labels. The CheXpert dataset was created with the participation of board-certified radiologists, resulting in the strong ground truth needed to train deep learning networks. Following the structured format of Datasheets for Datasets, this paper expands on the original CheXpert paper and other sources to show the critical role played by radiologists in the creation of reliable labels and to describe the different aspects of the dataset composition in detail. Such structured documentation intends to increase the awareness in the machine learning and medical communities of the strengths, applications, and evolution of CheXpert, thereby advancing the field of medical image analysis. Another objective of this paper is to put forward this dataset datasheet as an example to the community of how to create detailed and structured descriptions of datasets. We believe that clearly documenting the creation process, the contents, and applications of datasets accelerates the creation of useful and reliable models.