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

• URL: http://arxiv.org/abs/2403.09315v1
• Date: Thu, 14 Mar 2024 12:05:25 GMT
• Title: Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations
• Authors: Xinyu Xiong, Churan Wang, Wenxue Li, Guanbin Li,
• Abstract summary: We propose a semi- and weakly-supervised learning framework for mass segmentation. We use limited strongly-labeled samples and sufficient weakly-labeled samples to achieve satisfactory performance. Experiments on CBIS-DDSM and INbreast datasets demonstrate the effectiveness of our method.
• Score: 49.33388736227072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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