%0 Journal Article
%A Lakhani, Paras
%A Mongan, J
%A Singhal, C
%A Zhou, Q
%A Andriole, K P
%A Auffermann, W F
%A Prasanna, P M
%A Pham, T X
%A Peterson, Michael
%A Bergquist, P J
%A Cook, T S
%A Ferraciolli, S F
%A Corradi, G C A
%A Takahashi, M S
%A Workman, C S
%A Parekh, M
%A Kamel, S I
%A Galant, J
%A Mas-Sanchez, A
%A Benítez, E C
%A Sánchez-Valverde, M
%A Jaques, L
%A Panadero, M
%A Vidal, M
%A Culiañez-Casas, M
%A Angulo-Gonzalez, D
%A Langer, S G
%A de la Iglesia-Vayá, María
%A Shih, G
%T The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.
%D 2022
%U http://hdl.handle.net/10668/20430
%X 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.
%K Artificial Intelligence
%K COVID-19
%K Machine Learning
%K Pneumonia
%K Radiography
%K Thorax
%~