Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations
<|reference_start|>Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations: 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.<|reference_end|>
@article{xiong2024semi-,
    title={Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation
  with Limited Annotations},
    author={Xinyu Xiong, Churan Wang, Wenxue Li, Guanbin Li},
    journal={arXiv preprint arXiv:2403.09315},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09315},
    primaryClass={cs.CV}
}