1. The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs
- Author
-
Paras Lakhani, John Mongan, Chinmay Singhal, Quan Zhou, Katherine P. Andriole, William F. Auffermann, Prasanth Prasanna, Tessie Pham, Michael Peterson, Peter J. Bergquist, Tessa S. Cook, Suely Fazio Ferraciolli, Gustavo César de Antonio Corradi, Marcelo Takahashi, Spencer S Workman, Maansi Parekh, Sarah Kamel, Joaquin Herrero Galant, Alba Mas-Sanchez, Emi C. Benítez, Mariola Sánchez-Valverde, Lara Jaques, María Panadero, Marta Vidal, María Culiáñez-Casas, Diego M. Angulo-Gonzalez, Steve G. Langer, Maria de la Iglesia Vaya, and George Shih
- Subjects
Machine Learning ,Radiography ,Radiological and Ultrasound Technology ,Artificial Intelligence ,Radiologists ,Humans ,COVID-19 ,Radiology, Nuclear Medicine and imaging ,Radiography, Thoracic ,Pneumonia ,Thorax ,Computer Science Applications - 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 freely available to all researchers for academic and noncommercial use.
- Published
- 2021