Publication: The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.
Loading...
Identifiers
Date
2022-09-28
Authors
Lakhani, Paras
Mongan, J
Singhal, C
Zhou, Q
Andriole, K P
Auffermann, W F
Prasanna, P M
Pham, T X
Peterson, Michael
Bergquist, P J
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
Metrics
Export
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.
Description
MeSH Terms
Humans
COVID-19
Artificial Intelligence
Radiography
Machine Learning
Radiologists
Radiography, Thoracic
COVID-19
Artificial Intelligence
Radiography
Machine Learning
Radiologists
Radiography, Thoracic
DeCS Terms
CIE Terms
Keywords
Artificial Intelligence, COVID-19, Machine Learning, Pneumonia, Radiography, Thorax