The Oregon-Massachusetts Mammography Database (OMAMA-DB)

The OMAMA-DB contains thousands of breast cancer images with labels generated through a partnership with deep.health.

The data will be released in 2024.

Supported by

Outlier Detection for Mammograms (MIDL 2023)

Code available on github!

   

BibTex

@inproceedings{zurrin2023outlier,
title={Outlier Detection for Mammograms},
author={Ryan Zurrin and Neha Goyal and Pablo Bendiksen and Muskaan Manocha and Dan Simovici and Nurit Haspel and Marc Pomplun and Daniel Haehn},
abstract={Mammograms are vital for detecting breast cancer, the most common cancer among women in the US. However, low-quality scans and imaging artifacts can compromise their efficacy. We introduce an automated pipeline to filter low-quality mammograms from large datasets. Our initial dataset of 176,492 mammograms contained an estimated 5.5% lower quality scans with issues like metal coil frames, wire clamps, and breast implants. Manually removing these images is tedious and error-prone. Our two-stage process first uses threshold-based 5-bin histogram filtering to eliminate undesirable images, followed by a variational autoencoder to remove remaining low-quality scans. Our method detects such scans with an F1 Score of 0.8862 and preserves 163,568 high-quality mammograms. We provide results and tools publicly available as open-source software.},
booktitle={International Conference on Medical Imaging with Deep Learning},
year={2023},
url={https://openreview.net/forum?id=4E93Xdg98u},
code={https://github.com/mpsych/ODM},
shortvenue={MIDL 2023}
}

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