1. The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.
- Author
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Lakhani, Paras, Mongan, J, Singhal, C, Zhou, Q, Andriole, K, Auffermann, W, Prasanna, P, Pham, T, Peterson, Michael, Bergquist, P, Cook, T, Ferraciolli, S, Corradi, G, Takahashi, M, Workman, C, Parekh, M, Kamel, S, Galant, J, Mas-Sanchez, A, Benítez, E, Sánchez-Valverde, M, Jaques, L, Panadero, M, Vidal, M, Culiañez-Casas, M, Angulo-Gonzalez, D, Langer, S, de la Iglesia-Vayá, María, and Shih, G
- Subjects
Artificial Intelligence ,COVID-19 ,Machine Learning ,Pneumonia ,Radiography ,Thorax ,Humans ,COVID-19 ,Artificial Intelligence ,Radiography ,Machine Learning ,Radiologists ,Radiography ,Thoracic - Abstract
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.
- Published
- 2023