  Computed tomography (CT) has been widely explored as a COVID-19 screening and
assessment tool to complement RT-PCR testing. To assist radiologists with
CT-based COVID-19 screening, a number of computer-aided systems have been
proposed. However, many proposed systems are built using CT data which is
limited in both quantity and diversity. Motivated to support efforts in the
development of machine learning-driven screening systems, we introduce COVIDx
CT-3, a large-scale multinational benchmark dataset for detection of COVID-19
cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices from 6,068
patients across at least 17 countries, which to the best of our knowledge
represents the largest, most diverse dataset of COVID-19 CT images in
open-access form. Additionally, we examine the data diversity and potential
biases of the COVIDx CT-3 dataset, finding that significant geographic and
class imbalances remain despite efforts to curate data from a wide variety of
sources.
