Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion
  Segmentation
Xin Yue Qing Zhao Jianqiang Li Xiaoling Liu Changwei Song Suqin Liu Guanghui Fu
Breast cancer diagnosis challenges both patients and clinicians, with early
detection being crucial for effective treatment. Ultrasound imaging plays a key
role in this, but its utility is hampered by the need for precise lesion
segmentation-a task that is both time-consuming and labor-intensive. To address
these challenges, we propose a new framework: a morphology-enhanced, Class
Activation Map (CAM)-guided model, which is optimized using a computer vision
foundation model known as SAM. This innovative framework is specifically
designed for weakly supervised lesion segmentation in early-stage breast
ultrasound images. Our approach uniquely leverages image-level annotations,
which removes the requirement for detailed pixel-level annotation. Initially,
we perform a preliminary segmentation using breast lesion morphology knowledge.
Following this, we accurately localize lesions by extracting semantic
information through a CAM-based heatmap. These two elements are then fused
together, serving as a prompt to guide the SAM in performing refined
segmentation. Subsequently, post-processing techniques are employed to rectify
topological errors made by the SAM. Our method not only simplifies the
segmentation process but also attains accuracy comparable to supervised
learning methods that rely on pixel-level annotation. Our framework achieves a
Dice score of 74.39% on the test set, demonstrating compareable performance
with supervised learning methods. Additionally, it outperforms a supervised
learning model, in terms of the Hausdorff distance, scoring 24.27 compared to
Deeplabv3+'s 32.22. These experimental results showcase its feasibility and
superior performance in integrating weakly supervised learning with SAM. The
code is made available at: https://github.com/YueXin18/MorSeg-CAM-SAM.