# COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics

arXiv: https://arxiv.org/abs/2311.17677 · HTML: https://mlsanity.com/paper/2311.17677/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients](https://mlsanity.com/paper/2006.01174/) (2020)
- [COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics](https://mlsanity.com/paper/2206.03671/) (2022)

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

- [Chest X-ray Foundation Model with Global and Local Representations Integration](https://mlsanity.com/paper/2502.05142/) (2025)
- [MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging](https://mlsanity.com/paper/2604.13756/) (2026)
- [Enhancing Semantic Segmentation with Continual Self-Supervised Pre-training](https://mlsanity.com/paper/2509.17816/) (2025)
- [Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification](https://mlsanity.com/paper/2410.02805/) (2024)
- [Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging](https://mlsanity.com/paper/2505.21698/) (2025)
- [Which Similarity-Sensitive Entropy (Sentropy)?](https://mlsanity.com/paper/2511.03849/) (2025)

## Abstract (arXiv)

The global ramifications of the COVID-19 pandemic remain significant, exerting persistent pressure on nations even three years after its initial outbreak. Deep learning models have shown promise in improving COVID-19 diagnostics but require diverse and larger-scale datasets to improve performance. In this paper, we introduce COVIDx CXR-4, an expanded multi-institutional open-source benchmark dataset for chest X-ray image-based computer-aided COVID-19 diagnostics. COVIDx CXR-4 expands significantly on the previous COVIDx CXR-3 dataset by increasing the total patient cohort size by greater than 2.66 times, resulting in 84,818 images from 45,342 patients across multiple institutions. We provide extensive analysis on the diversity of the patient demographic, imaging metadata, and disease distributions to highlight potential dataset biases. To the best of the authors' knowledge, COVIDx CXR-4 is the largest and most diverse open-source COVID-19 CXR dataset and is made publicly available as part of an open initiative to advance research to aid clinicians against the COVID-19 disease.