The Society for Imaging Informatics in Medicine has partnered with The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), and the Radiological Society of North America (RSNA) to host a Machine Learning Challenge on COVID-19 Pneumonia Detection and Localization on Kaggle. The competition used augmented annotations on the public chest radiograph datasets from the Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID-19 Open Radiology Database (RICORD) and BIMCV-COVID-19 Dataset, created by an international group of volunteer radiologists from Brazil, Spain, and the U.S. using a commercial web-based tool from MD.ai.
The SIIM-FISABIO-RSNA Covid-19 Detection Challenge required teams to develop high quality computer vision models to detect and localize COVID-19 pneumonia to help doctors provide a quick and confident diagnosis. 1,786 participants on 1,305 teams from 82 countries took part in the competition.
This challenge was supported by the National Science Foundation (NSF) Convergence Accelerator Grant that SIIM, along with its collaborators, was awarded in September 2020. SIIM’s Corporate Impact Partners, HP and Intel, provided $100,000 in prizes, in addition to a special prize - a high-end workstation for the Best Student Team.
Top 10 Winning Teams
The Top 10 winning teams took home a total of $100,000 in prize money.
Winning algorithms are being open sourced to improve quality of and efficiency in healthcare.
Want to learn more about the curation, annotation methodology and characteristics of the dataset used in this challenge?