  Detecting polyps through colonoscopy is an important task in medical image
segmentation, which provides significant assistance and reference value for
clinical surgery. However, accurate segmentation of polyps is a challenging
task due to two main reasons. Firstly, polyps exhibit various shapes and
colors. Secondly, the boundaries between polyps and their normal surroundings
are often unclear. Additionally, significant differences between different
datasets lead to limited generalization capabilities of existing methods. To
address these issues, we propose a segmentation model based on Prompt-Mamba,
which incorporates the latest Vision-Mamba and prompt technologies. Compared to
previous models trained on the same dataset, our model not only maintains high
segmentation accuracy on the validation part of the same dataset but also
demonstrates superior accuracy on unseen datasets, exhibiting excellent
generalization capabilities. Notably, we are the first to apply the
Vision-Mamba architecture to polyp segmentation and the first to utilize prompt
technology in a polyp segmentation model. Our model efficiently accomplishes
segmentation tasks, surpassing previous state-of-the-art methods by an average
of 5% across six datasets. Furthermore, we have developed multiple versions of
our model with scaled parameter counts, achieving better performance than
previous models even with fewer parameters. Our code and trained weights will
be released soon.
