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The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.

Paras LakhaniJ MonganC SinghalQ ZhouK P AndrioleW F AuffermannP M PrasannaT X PhamMichael PetersonP J BergquistT S CookS F FerraciolliG C A CorradiM S TakahashiC S WorkmanM ParekhS I KamelJ GalantA Mas-SanchezE C BenítezM Sánchez-ValverdeL JaquesM PanaderoM VidalM Culiañez-CasasD Angulo-GonzalezS G LangerMaría de la Iglesia-VayáG Shih
Published in: Journal of digital imaging (2022)
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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