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

Overview

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.

publication date

  • September 28, 2022

Research

keywords

  • COVID-19

Identity

PubMed Central ID

  • PMC9518934

Scopus Document Identifier

  • 85139094523

Digital Object Identifier (DOI)

  • 10.1148/radiol.2020201160

PubMed ID

  • 36171520

Additional Document Info

volume

  • 36

issue

  • 1