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

COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images