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Related Experiment Video

Updated: May 29, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

OMAMA-DB: the Oregon-Massachusetts Mammography Database.

Avanith Kanamarlapudi¹, Ryan Zurrin¹, Edward Gaibor¹

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

Journal of Medical Imaging (Bellingham, Wash.)

|May 28, 2026

Summary

This summary is machine-generated.

A new large-scale mammography dataset, OMAMA-DB, aids artificial intelligence (AI) development for breast cancer screening. This curated dataset with pathology labels and lesion annotations enables reliable AI model training and evaluation.

Keywords:

DeepSight MedGemma OMAMA-DB breast cancer detection dataset curation medical imaging outlier detection three-dimensional tomosynthesis volumes two-dimensional mammograms

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Clinical Imaging of Microwave Mammography

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Published on: November 14, 2025

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Public datasets for training AI in breast cancer screening are often limited in size and quality.
Developing reliable AI systems for mammography requires extensive, high-quality data.

Purpose of the Study:

Introduce OMAMA-DB, a comprehensive public dataset of 2D mammograms and 3D tomosynthesis volumes.
Provide a resource to overcome limitations in existing datasets for AI model development in breast cancer screening.

Main Methods:

Curated 231,080 images from 967,991 initial images using multi-stage filtering.
Applied histogram filtering and variational autoencoder for outlier detection in 2D images.
Generated pathology-based cancer labels and automated lesion annotations, with expert validation via a web tool.

Main Results:

OMAMA-DB contains 231,080 images, including 7351 2D and 374 3D cancer cases.
Fine-tuned MedGemma achieved high performance (accuracy 0.989, sensitivity 0.997, F1 0.989) on a balanced subset.
Automated methods significantly outperformed human accuracy in classifying real vs. synthetic mammograms.

Conclusions:

OMAMA-DB offers a valuable resource for medical imaging research in mammography.
Fine-tuned foundation models show strong performance, emphasizing the need for real clinical data.
Open availability of data, models, and parameters supports further research and development.