RT Journal Article
T1 The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.
A1 Lakhani, Paras
A1 Mongan, J
A1 Singhal, C
A1 Zhou, Q
A1 Andriole, K P
A1 Auffermann, W F
A1 Prasanna, P M
A1 Pham, T X
A1 Peterson, Michael
A1 Bergquist, P J
A1 Cook, T S
A1 Ferraciolli, S F
A1 Corradi, G C A
A1 Takahashi, M S
A1 Workman, C S
A1 Parekh, M
A1 Kamel, S I
A1 Galant, J
A1 Mas-Sanchez, A
A1 Benítez, E C
A1 Sánchez-Valverde, M
A1 Jaques, L
A1 Panadero, M
A1 Vidal, M
A1 Culiañez-Casas, M
A1 Angulo-Gonzalez, D
A1 Langer, S G
A1 de la Iglesia-Vayá, María
A1 Shih, G
K1 Artificial Intelligence
K1 COVID-19
K1 Machine Learning
K1 Pneumonia
K1 Radiography
K1 Thorax
AB 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.
YR 2022
FD 2022-09-28
LK http://hdl.handle.net/10668/20430
UL http://hdl.handle.net/10668/20430
LA en
DS RISalud
RD Apr 5, 2025