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© 2023 Nightingale Open Science. All rights reserved.
We are pleased to announce the results of the 2023 Health Equity Data Challenge presented by NIH AIM-AHEAD Data Science Training Core and Nightingale Open Science. The objective of the challenge was to predict the breast cancer stage from the biopsy slide images. The following teams win the top 3 places and prizes. They are also invited to present at the NIH AIM-AHEAD meeting in Bethesda, MD from August 1 to 3, 2023.
Breast cancer health disparities and inequities exist on many levels, including race, ethnicity, socioeconomic status, geography, and access to healthcare. For example, Black women are more likely to die from breast cancer than white women, despite having a lower incidence of the disease. This is due to a combination of factors, including differences in access to healthcare, late diagnosis, and more aggressive forms of the disease. Triple negative breast cancers (TNBC) are characterized by the absence or low levels of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor 2 receptors (HER2) on the tumor cells. According to the American Cancer Society, tThey constitute about 15% to 20% of breast cancers and are among the hardest subtype to treat. They have higher incidence in African American and Hispanic women, with African Americans facing worse outcomes than other race and ethnic groups.
NIH AIM-AHEAD and Nightingale developed this challenge in order to catalyze the development of algorithms that find new signals in digital pathology images, ultimately providing new insights into which minority patients may be at risk and need preventive treatment. The winning solutions are published as open source by their authors to promote collaborative and repeatable research.
Team name: Wombcare Team
Team members: Yeno Gbenou
Organization: Drexel University
Advised by Bonaventure Dossou, McGill University, Mila Quebec AI Institute
Code on GitHub
Team name: Bison Breast Cancer Prediction Team
Team Members: Bryan Mildort
Organization:Organization: Howard University
Code on GitHub
Team name: University of Hawaii AIM-AHEAD Health Equity Team
Team members: Arianna Bunnell, Zain Jabbar, Armin Soltan
Organization: University of Hawaiʻi
Code on GitHub
Thanks to the NIH AIM-AHEAD Training Core for partnering with us to host this ML challenge. Thanks to the data provider Providence St. Joseph Health, and to Gordon and Betty Moore Foundation's Diagnostic Excellence Initiative for the funding support to publish this data set.
Congratulations to the winners!