# VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations

arXiv: https://arxiv.org/abs/2012.15029 · HTML: https://mlsanity.com/paper/2012.15029/

## Key facts

- Cited by 239 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 30 of 239)

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- [CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging](https://mlsanity.com/paper/2403.06801/) (2024)
- [CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training](https://mlsanity.com/paper/2310.13292/) (2023)
- [Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports](https://mlsanity.com/paper/2111.03452/) (2021)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
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- [PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation](https://mlsanity.com/paper/2411.05085/) (2024)
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- [REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays](https://mlsanity.com/paper/2109.14187/) (2021)
- [CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images](https://mlsanity.com/paper/2307.03293/) (2023)
- [Explainable Deep Learning Methods in Medical Image Classification: A Survey](https://mlsanity.com/paper/2205.04766/) (2022)
- [Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis](https://mlsanity.com/paper/2303.11224/) (2023)
- [Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review](https://mlsanity.com/paper/2209.13177/) (2022)
- [VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels](https://mlsanity.com/paper/2102.11467/) (2021)
- [MedIAnomaly: A comparative study of anomaly detection in medical images](https://mlsanity.com/paper/2404.04518/) (2024)
- [RadVLM: A Multitask Conversational Vision-Language Model for Radiology](https://mlsanity.com/paper/2502.03333/) (2025)
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- [Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging](https://mlsanity.com/paper/2402.14815/) (2024)
- [Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2410.18387/) (2024)

## Abstract (arXiv)

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available (https://www.physionet.org/content/vindr-cxr/1.0.0/) in DICOM format along with the labels of both the training set and the test set.