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

arXiv: https://arxiv.org/abs/2403.09315 · HTML: https://mlsanity.com/paper/2403.09315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Semi-Supervised Semantic Image Segmentation with Self-correcting Networks](https://mlsanity.com/paper/1811.07073/) (2018)
- [AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms](https://mlsanity.com/paper/1810.10151/) (2018)
- [Label-efficient Hybrid-supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2203.05956/) (2022)
- [A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation](https://mlsanity.com/paper/2007.10007/) (2020)
- [Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification](https://mlsanity.com/paper/2009.14406/) (2020)

## Built on by (most cited first, 1 of 1)

- [Paired Image Generation with Diffusion-Guided Diffusion Models](https://mlsanity.com/paper/2507.14833/) (2025)

## Abstract (arXiv)

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.