RSNA Screening Mammography Breast Cancer Detection AI Challenge (2023)

The 2023 RSNA Screening Mammography Breast Cancer Detection AI Challenge invited participants to develop AI models that can aid in the detection of breast cancer and was conducted on a platform provided by Kaggle, Inc.

This AI challenge attracted 2,146 competitors forming 1,687 teams; the greatest number of competitors since RSNA organized its first AI challenge in 2017. 

About the 2023 AI Challenge

Breast cancer is the most commonly occurring cancer worldwide, according to the World Health Organization. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Early detection and treatment are critical to reducing cancer fatalities.

“Although there is a worldwide shortage of radiologists to interpret screening mammograms, radiologists remain concerned about how well AI systems will work in their patient population,” said Dr. Linda Moy, a professor of radiology at the NYU Grossman School of Medicine and editor of the journal Radiology. “This diverse, well-curated dataset may be used to assess the generalizability to diverse patient populations. The RSNA Screening Mammography Breast Cancer Detection AI Challenge will catalyze collaboration to improve the diagnostic accuracy of screening mammography and save patients’ lives.”

Machine learning and AI tools can also help streamline the process radiologists use to evaluate screening mammograms. “The number of participants we had in this competition was amazing, and reflects the high levels of interest in using large, high-quality datasets to advance the state of the art in mammographic diagnosis,” said John Mongan, MD, PhD, a professor of radiology at the University of California, San Francisco and chair of the RSNA Machine Learning Steering Committee. “We expect that the dataset and the work of the contestants will provide an ideal foundation for rapid advance in breast imaging AI.” 

About the imaging data

The dataset was contributed by mammography screening programs in Australia and the U.S. It includes detailed labels, with radiologists’ evaluations and follow-up pathology results for suspected malignancies.

The accuracy of machine learning models developed by contestants to detect cancer will be evaluated against this ground truth dataset. At the conclusion of the challenge, the dataset will remain available for use in further research.

This challenge is part of a broader research project that will examine how models generated in the competition perform against previously unseen data and compare their performance to that of expert human observers. These questions are critical in determining how AI tools will perform in clinical settings.


The dataset description is coming soon.

Winning teams and entries

Team name Solution
mr.robot Solution
cancerdetectman Solution
H.B.M.F Solution
CDI Solution
Racers Solution
Chiral Mistrals Solution
luddite&MT Solution
BCC Solution
Team name: mr.robot
Solution: Solution
Team name: cancerdetectman
Solution: Solution
Team name: H.B.M.F
Solution: Solution
Team name: CDI
Solution: Solution
Team name: Racers
Solution: Solution
Team name: Chiral Mistrals
Solution: Solution
Team name: luddite&MT
Solution: Solution
Team name: BCC
Solution: Solution


Access the challenge results on the Kaggle website.

Access results


RSNA would like to thank all those who made this challenge possible.

View acknowledgments

Recognition for winning teams

The eight teams who submitted the highest-scoring algorithms shared in $50,000 total prize money. The teams will be recognized at an event during RSNA 2023 (Nov. 26–30, 2023). 

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