  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.
