# COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics

arXiv: https://arxiv.org/abs/2206.03671 · HTML: https://mlsanity.com/paper/2206.03671/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 8 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients](https://mlsanity.com/paper/2006.01174/) (2020)
- [COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.06640/) (2021)
- [COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity](https://mlsanity.com/paper/2005.12855/) (2020)
- [COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.00256/) (2021)
- [MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis](https://mlsanity.com/paper/2110.06063/) (2021)

## Built on by (most cited first, 10 of 10)

- [SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model](https://mlsanity.com/paper/2211.12944/) (2022)
- [COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics](https://mlsanity.com/paper/2311.17677/) (2023)
- [A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation](https://mlsanity.com/paper/2604.00493/) (2026)
- [DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies](https://mlsanity.com/paper/2402.15534/) (2024)
- [Learning Low-Rank Feature for Thorax Disease Classification](https://mlsanity.com/paper/2404.18933/) (2024)
- [X-WIN: Building Chest Radiograph World Model via Predictive Sensing](https://mlsanity.com/paper/2511.14918/) (2025)
- [Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning](https://mlsanity.com/paper/2402.14741/) (2024)
- [Analysing Environmental Efficiency in AI for X-Ray Diagnosis](https://mlsanity.com/paper/2511.07436/) (2025)
- [Study of Vision Transformers for Covid-19 Detection from Chest X-rays](https://mlsanity.com/paper/2307.09402/) (2023)
- [Improving Medical Visual Representations via Radiology Report Generation](https://mlsanity.com/paper/2310.19635/) (2023)

## Abstract (arXiv)

After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing but has greatly increased due to its routine clinical use for respiratory complaints. Thus far, many visual perception models have been proposed for COVID-19 screening based on CXR imaging. Nevertheless, the accuracy and the generalization capacity of these models are very much dependent on the diversity and the size of the dataset they were trained on. Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 is composed of 30,386 CXR images from a multinational cohort of 17,026 patients from at least 51 countries, making it, to the best of our knowledge, the most extensive, most diverse COVID-19 CXR dataset in open access form. Here, we provide comprehensive details on the various aspects of the proposed dataset including patient demographics, imaging views, and infection types. The hope is that COVIDx CXR-3 can assist scientists in advancing machine learning research against both the COVID-19 pandemic and related diseases.