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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;
- 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;
- Shih, G
- et al.
Published Web Location
https://doi.org/10.1007/s10278-022-00706-8Abstract
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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