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.

翻译：乳腺肿块的精准识别对于乳腺癌诊断至关重要，然而其尺寸微小且常隐匿于周围正常腺体组织中的特性使其成为一项挑战。更棘手的是，在临床实践中获取足量像素级标注以训练深度神经网络成本高昂。为同时攻克这两个难题，我们提出了一种兼具半监督与弱监督特性的肿块分割框架，该框架通过利用少量强标注样本与足量弱标注样本即可达到令人满意的性能。该框架包含用于排除病灶无关背景区域的辅助分支、输出最终预测结果的分割分支，以及融合两分支互补信息的空间提示模块。我们进一步将编码得到的模糊特征解耦为病灶相关特征与无关特征以提升性能。在CBIS-DDSM与INbreast数据集上的实验验证了本方法的有效性。