COVID-19 AI Detection Challenge Open for Submissions

The COVID-19 AI Detection Challenge is available for researchers to access and develop their models.

RSNA in partnership with the Society for Imaging Informatics in Medicine (SIIM) and The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO) is hosting the competition, also called the Machine Learning Challenge on COVID-19 Pneumonia Detection and Localization. The competition will use 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.

Challenge participants will develop high quality computer vision models to detect and localize COVID-19 pneumonia to help doctors provide a quick and confident diagnosis, thus improving patient care by enabling the right treatment before the most severe effects of the virus take hold.

RSNA, SIIM and FISABIO will use their respective talents and resources to promote deployment of the winning algorithms into clinical use for the benefit of the greater medical imaging community, improving quality and efficiency in healthcare.

This challenge is supported by the National Science Foundation (NSF) Convergence Accelerator Grant that SIIM, along with its collaborators, was awarded. SIIM’s Corporate Impact Partners, HP and Intel, are providing $100,000 in prizes to bring awareness to SIIM’s call for open-source AI models to populate the prototype Model Zoo built as a result of Phase 1 of this grant. The Model Zoo will serve as the basis for creating a clinical research testing of collaborative model-centric AI platform to meet the urgent needs of scalable validation and translation of model-centric AI in medical imaging. All competitors are encouraged to submit their open-source models to the prototype Model Zoo.

“RSNA is pleased to collaborate on this very important AI challenge,” said John Mongan, MD, PhD, chair of the RSNA machine learning steering subcommittee and vice chair for informatics and associate professor of radiology at the University of California, San Francisco. “COVID-19 has dramatically impacted the way we conduct our personal and professional lives. RSNA developed RICORD as a multinational, multi-institutional, expert-annotated COVID-19 imaging data set designed for the AI community. Having freely available, comprehensive medical imaging data sets for use in challenges like this is an important step toward using AI to improve patient outcomes.”

"As the COVID-19 pandemic continues to impact our lives, there is potential for artificial intelligence (AI) based solutions to help frontline clinicians across the world in managing COVID-19 patients, whether it is facilitating diagnosis, affecting treatment decisions or prognosticating outcomes," said Paras Lakhani, MD, associate professor of radiology, Thomas Jefferson University Hospital, SIIM Machine Learning Steering Committee Member and Annotation Project Lead.

“This initiative will demonstrate what it means to work as a team, the potential of AI to aid diagnosis and therefore, will allow public authorities to make a better use of health data for research purposes, and how it can contribute to the digital transformation," said Maria de la Iglesia Vayá, PhD and IP from Biomedical Imaging Lab, FISABIO-CIPF.

“The SIIM-FISABIO-RSNA Kaggle Challenge is the perfect example of the good that can be done when the data science community comes together with a common goal and we’re thrilled to be able to support the winners with well-deserved recognition from Z by HP and Intel for their efforts," said Jeri Culp, head of data science, Advanced Compute and Solutions, HP Inc. 

The winners will be presented on Sept. 19-20 at the 2021 SIIM Conference on Machine Intelligence in Medical Imaging.

For More Information

Learn more about the COVID-19 AI Detection Challenge at RSNA.org/AI-Challenge. 

Read RSNA News stories on previous challenge winners:

• In Fourth Year, RSNA AI Challenge Propels Winners to Greater Heights
• RSNA AI Challenge Breaks New Ground
• Ensembling Improves Machine Learning Model Performance