Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations
This addresses the challenge of expensive annotation in clinical breast cancer diagnosis, but it is incremental as it builds on existing semi- and weakly-supervised methods.
The paper tackles the problem of segmenting breast masses in mammograms with limited pixel-wise annotations by proposing a semi- and weakly-supervised learning framework, achieving satisfactory performance on CBIS-DDSM and INbreast datasets.
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.