  Polyp segmentation plays a vital role in accurately locating polyps at an
early stage, which holds significant clinical importance for the prevention of
colorectal cancer. Various polyp segmentation methods have been developed using
fully-supervised deep learning techniques. However, pixel-wise annotation for
polyp images by physicians during the diagnosis is both time-consuming and
expensive. Moreover, visual foundation models such as the Segment Anything
Model (SAM) have shown remarkable performance. Nevertheless, directly applying
SAM to medical segmentation may not produce satisfactory results due to the
inherent absence of medical knowledge. In this paper, we propose a novel
SAM-guided Collaborative Learning Network (SAM-CLNet) for scribble-supervised
polyp segmentation, enabling a collaborative learning process between our
segmentation network and SAM to boost the model performance. Specifically, we
first propose a Cross-level Enhancement and Aggregation Network (CEA-Net) for
weakly-supervised polyp segmentation. Within CEA-Net, we propose a Cross-level
Enhancement Module (CEM) that integrates the adjacent features to enhance the
representation capabilities of different resolution features. Additionally, a
Feature Aggregation Module (FAM) is employed to capture richer features across
multiple levels. Moreover, we present a box-augmentation strategy that combines
the segmentation maps generated by CEA-Net with scribble annotations to create
more precise prompts. These prompts are then fed into SAM, generating
segmentation SAM-guided masks, which can provide additional supervision to
train CEA-Net effectively. Furthermore, we present an Image-level Filtering
Mechanism to filter out unreliable SAM-guided masks. Extensive experimental
results show that our SAM-CLNet outperforms state-of-the-art weakly-supervised
segmentation methods.
