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

Autor:	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., Cook, T. S., Ferraciolli, S. F., Corradi, G. C. A., Takahashi, MS, Workman, C. S., Parekh, M., Kamel, S. I., Galant, J., Mas-Sanchez, A., Benítez, E. C.
Předmět:	PNEUMONIA CHEST X rays COVID-19 CONFIDENCE intervals ARTIFICIAL intelligence MACHINE learning INTER-observer reliability DESCRIPTIVE statistics RESEARCH funding DATA analysis software COVID-19 pandemic
Zdroj:	Journal of Digital Imaging; Feb2023, Vol. 36 Issue 1, p365-372, 8p, 4 Black and White Photographs, 1 Diagram, 2 Charts
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink