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Abstract #0962

Improved Detection and Localization of Artifacts using CutArt Augmentation

Ben A Duffy1, Greg Zaharchuk2, Enhao Gong1, and Keshav Datta1
1Subtle Medical Inc., Menlo Park, CA, United States, 2Stanford University, Stanford, CA, United States


With the aim of improving the performance of an automated quality control system, we propose to use a data augmentation technique based on cropped patches of simulated artifacts (CutArt) instead of artifacts that are distributed across the entire image. This has the advantage of improving the artifact localization and quality control classification performance, as assessed by experiments on simulated as well as real artifact affected data. Localization experiments suggested that the CutArt model learns to focus on the tissue of interest instead of the image background.

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