  Ultrasound imaging plays a critical role in the early detection of breast
cancer. Accurate identification and segmentation of lesions are essential steps
in clinical practice, requiring methods to assist physicians in lesion
segmentation. However, ultrasound lesion segmentation models based on
supervised learning require extensive manual labeling, which is both
time-consuming and labor-intensive. In this study, we present a novel framework
for weakly supervised lesion segmentation in early breast ultrasound images.
Our method uses morphological enhancement and class activation map (CAM)-guided
localization. Finally, we employ the Segment Anything Model (SAM), a computer
vision foundation model, for detailed segmentation. This approach does not
require pixel-level annotation, thereby reducing the cost of data annotation.
The performance of our method is comparable to supervised learning methods that
require manual annotations, achieving a Dice score of 74.39% and outperforming
comparative supervised models in terms of Hausdorff distance in the BUSI
dataset. These results demonstrate that our framework effectively integrates
weakly supervised learning with SAM, providing a promising solution for breast
cancer image analysis. The code for this study is available at:
https://github.com/YueXin18/MorSeg-CAM-SAM.
