A Progressive Soft Erase and Multi-Scale Feature Fusion Method for Medical Education Management of Breast Cancer

Breast ultrasound (BUS) image lesion segmentation is an important step in computer-aided diagnosis (CAD) systems for breast cancer screening. Due to acquiring pixel-level labels for fully supervised BUS lesion segmentation being extremely expensive and time-consuming, many studies have adopted weakly supervised methods to mitigate the reliance of models on pixel-level labels. However, in weakly supervised methods, the class activation map (CAM) often excessively focuses on the most distinctive regions of the target while overlooking other lesion parts. This limitation often leads to insufficient CAM activation and compromises the accuracy of lesion segmentation. To address this problem, we propose a weakly supervised framework for lesion segmentation on BUS images using image-level labels. Specifically, the method consists of two stages: classification and segmentation. During the classification stage, the network based on the progressive soft erase (PSE) module reduces the contribution of the most discriminative feature region in CAM, making the network focus on the features of non-dominant regions and generating more high-quality pseudo-masks. In the segmentation stage, we propose a dual-branch feature fusion (DBFF) network, which can not only capture more contextual information by fusing from different scales but also generate a more accurate segmentation mask. Extensive experiments on the publicly available dataset BUS I show the effectiveness of our method. Furthermore, this model significantly supports medical education management by providing a more efficient and cost-effective approach to breast lesion segmentation.