COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark
  Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics
Yifan Wu Hayden Gunraj Chi-en Amy Tai Alexander Wong
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.