Morphology-based losses for weakly supervised segmentation of mammogramsDownload PDF

Published: 11 May 2021, Last Modified: 16 May 2023MIDL 2021 PosterReaders: Everyone
Keywords: Mammography, Weakly supervised learning, Segmentation, Morphology
TL;DR: Morphological operators such as top-hat and closing allow to improve the precision of segmentation in a weakly supervised setting.
Abstract: Segmentation is one of the most common tasks in medical imaging, but it often requires expensive ground truth for training. Weakly supervised methods cope with the lack of annotations, however, they often fall short compared to fully supervised ones. In this work, we propose to constrain the segmentation output with morphological operations, leading to an increase in the overall performance. In particular, we use top-hat and closing operations. We evaluate the method on high-resolution images from INBreast dataset and achieve an increase in F$_1$ of $\approx 0.14$ and in recall of $\approx 0.22$ compared to the training without morphology loss.
Paper Type: methodological development
Primary Subject Area: Segmentation
Secondary Subject Area: Application: Radiology
Paper Status: based on accepted/submitted journal paper
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