The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms

• URL: http://arxiv.org/abs/2202.04073v1
• Date: Tue, 8 Feb 2022 14:40:59 GMT
• Title: The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms
• Authors: Jiwoong J. Jeong, Brianna L. Vey, Ananth Reddy, Thomas Kim, Thiago Santos, Ramon Correa, Raman Dutt, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, Minjae Woo, Christopher R. McAdams, Mary S. Newell, Imon Banerjee, Judy Gichoya, Hari Trivedi
• Abstract summary: The EMory BrEast imaging dataset contains 3650,000 2D and diagnostic mammograms for 116,000 women divided equally between White and African American patients. Our goal is to share this dataset with research partners to aid in development and validation of breast AI models that will serve all patients fairly and help decrease bias in medical AI.
• Score: 2.243792799100692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse. To date, the majority of publicly available breast imaging datasets lack in one or more of these areas. Models trained on these data may therefore underperform on patient populations or pathologies that have not previously been encountered. The EMory BrEast imaging Dataset (EMBED) addresses these gaps by providing 3650,000 2D and DBT screening and diagnostic mammograms for 116,000 women divided equally between White and African American patients. The dataset also contains 40,000 annotated lesions linked to structured imaging descriptors and 61 ground truth pathologic outcomes grouped into six severity classes. Our goal is to share this dataset with research partners to aid in development and validation of breast AI models that will serve all patients fairly and help decrease bias in medical AI.

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