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Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations

Author:
Xinyu Xiong, Churan Wang, Wenxue Li, Guanbin Li
Keyword:
Computer Science, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (cs.CV)
journal:
--
date:
2024-03-14 00:00:00
Abstract
Accurate identification of breast masses is crucial in diagnosing breast cancer; however, it can be challenging due to their small size and being camouflaged in surrounding normal glands. Worse still, it is also expensive in clinical practice to obtain adequate pixel-wise annotations for training deep neural networks. To overcome these two difficulties with one stone, we propose a semi- and weakly-supervised learning framework for mass segmentation that utilizes limited strongly-labeled samples and sufficient weakly-labeled samples to achieve satisfactory performance. The framework consists of an auxiliary branch to exclude lesion-irrelevant background areas, a segmentation branch for final prediction, and a spatial prompting module to integrate the complementary information of the two branches. We further disentangle encoded obscure features into lesion-related and others to boost performance. Experiments on CBIS-DDSM and INbreast datasets demonstrate the effectiveness of our method.
PDF: Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations.pdf
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