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Lakhani, Paras; Mongan, J.; Singhal, C.; Zhou, Q.; Andriole, K. P.; Auffermann, W. F.; Prasanna, P. M.; Pham, T. X.; Peterson, Michael; Bergquist, P. J.; Cook, T. S.; Ferraciolli, S. F.; Corradi, G. C. A.; Takahashi, MS; Workman, C. S.; Parekh, M.; Kamel, S. I.; Galant, J.; Mas-Sanchez, A.; Benítez, E. C.; Sánchez-Valverde, M.; Jaques, L.; Panadero, M.; Vidal, M.; Culiañez-Casas, M.; Angulo-Gonzalez, D.; Langer, S. G.; de la Iglesia-Vayá, María; Shih, G.
Journal of digital imaging, 02/2023, Letnik: 36, Številka: 1Journal Article
We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including “typical,” “indeterminate,” and “atypical appearance” for COVID-19, or “negative for pneumonia,” adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use.
