The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.
- 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, M S
- 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
Publication date
September 2022
DOI Publisher
Springer Science and Business Media LLC Journal
Journal of Digital ImagingAbstract
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
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