  Polyp segmentation, a critical concern in medical imaging, has prompted
numerous proposed methods aimed at enhancing the quality of segmented masks.
While current state-of-the-art techniques produce impressive results, the size
and computational cost of these models pose challenges for practical industry
applications. Recently, the Segment Anything Model (SAM) has been proposed as a
robust foundation model, showing promise for adaptation to medical image
segmentation. Inspired by this concept, we propose SAM-EG, a framework that
guides small segmentation models for polyp segmentation to address the
computation cost challenge. Additionally, in this study, we introduce the Edge
Guiding module, which integrates edge information into image features to assist
the segmentation model in addressing boundary issues from current segmentation
model in this task. Through extensive experiments, our small models showcase
their efficacy by achieving competitive results with state-of-the-art methods,
offering a promising approach to developing compact models with high accuracy
for polyp segmentation and in the broader field of medical imaging.
