Detalle de: The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs
| Title | The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs |
| 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. |
| Apa Reference | Lakhani, P., Mongan, J., Singhal, C., Zhou, Q., Andriole, K.P., Auffermann, W.F., Prasanna, P.M., Pham, T.X., Peterson, M., Bergquist, P.J., Cook, T.S., Ferraciolli, S.F., Corradi, G.C.A., Takahashi, M., 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, M. & Shih, G. (2022). The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs. Journal of Digital Imaging, 36(1), 365-372. 10.1007/s10278-022-00706-8 |
| Doi | 10.1007/s10278-022-00706-8 |
| Publication Date | 28 Sep 2022 |
| Publication Type | Article |
| Researcher | Maria de-la-Iglesia-Vaya |
| Ontology | Artificial Intelligence, Machine Learning in Biology |
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