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

TL;DR

COVIDx CXR-4 is the largest and most diverse open-source chest X-ray dataset for COVID-19 diagnostics, significantly expanding previous datasets to support improved deep learning models.

Contribution

This paper introduces COVIDx CXR-4, a large, multi-institutional dataset that enhances diversity and size for COVID-19 chest X-ray analysis, addressing previous dataset limitations.

Findings

Expanded dataset size by over 2.66 times

Includes 84,818 images from 45,342 patients

Highlights potential dataset biases

Abstract

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.