The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.
Paras Lakhani, John Mongan, Chinmay Singhal, Quan Zhou, Katherine P. Andriole, William F. Auffermann, Prasanth Prasanna, Tessie Pham, Michael Peterson, Peter Bergquist, Tessa S. Cook, Suely Fazio Ferraciolli, Gustavo César de Antonio Corradi, Marcelo Straus 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, María de la Iglesia-Vayá, George Shih
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.