Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation
  with Limited Annotations
Xinyu Xiong Churan Wang Wenxue Li Guanbin Li
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.