OMAMA-DB: the Oregon-Massachusetts Mammography Database.

Avanith Kanamarlapudi¹, Ryan Zurrin¹, Edward Gaibor¹, Benjamin Bendiksen Gutierrez¹, Neha Goyal¹, Vidhya Sree Narayanapa¹, Dan Simovici¹, Nurit Haspel¹, Marc Pomplun¹, Hyunkwang Lee², Mack Bandler³, Greg Sorensen², Daniel Haehn¹

¹University of Massachusetts Boston, Department of Computer Science, Boston, Massachusetts, United States.

²DeepHealth/RadNet, Boston, Massachusetts, United States.

³Medford Radiology Group, Medford, Oregon, United States.

PubMed PMC DOI PDF

Abstract

PURPOSE: Public datasets for training artificial intelligence (AI) models in breast cancer screening are limited in size and quality, making it difficult to develop reliable systems. We introduce OMAMA-DB, an extensive publicly available collection of two-dimensional (2D) mammograms and three-dimensional (3D) tomosynthesis volumes. APPROACH: Starting from 967,991 images, we created a curated set…

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