S009 - Outlier Detection for Mammograms

Ryan Zurrin, Neha Goyal, Pablo Bendiksen, Muskaan Manocha, Dan Simovici, Nurit Haspel, Marc Pomplun, Daniel Haehn

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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.
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Schedule: Tuesday, July 11: Posters — 10:30–12:00 & 15:00–16:00
Poster location: T34