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The global ramifications of the COVID-19 pandemic remain significant,exerting persistent pressure on nations even three years after its initialoutbreak. Deep learning models have shown promise in improving COVID-19diagnostics but require diverse and larger-scale datasets to improveperformance. In this paper, we introduce COVIDx CXR-4, an expandedmulti-institutional open-source benchmark dataset for chest X-ray image-basedcomputer-aided COVID-19 diagnostics. COVIDx CXR-4 expands significantly on theprevious COVIDx CXR-3 dataset by increasing the total patient cohort size bygreater than 2.66 times, resulting in 84,818 images from 45,342 patients acrossmultiple institutions. We provide extensive analysis on the diversity of thepatient demographic, imaging metadata, and disease distributions to highlightpotential dataset biases. To the best of the authors' knowledge, COVIDx CXR-4is the largest and most diverse open-source COVID-19 CXR dataset and is madepublicly available as part of an open initiative to advance research to aidclinicians against the COVID-19 disease.

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