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.

翻译：通过结肠镜检测息肉是医学图像分割中的一项重要任务，为临床手术提供了重要的辅助和参考价值。然而，由于两个主要原因，息肉的精确分割极具挑战性。首先，息肉具有多样的形态和颜色。其次，息肉与正常周围组织之间的边界往往不清晰。此外，不同数据集之间的显著差异导致现有方法的泛化能力有限。针对这些问题，我们提出了一种基于Prompt-Mamba的分割模型，该模型融合了最新的Vision-Mamba和提示技术。与以往在同一数据集上训练的模型相比，我们的模型不仅在同一数据集的验证部分保持了较高的分割精度，而且在未见过的数据集上也展现了优越的准确率，呈现出优异的泛化能力。值得注意的是，我们是首次将Vision-Mamba架构应用于息肉分割，并首次在息肉分割模型中利用提示技术。我们的模型高效地完成了分割任务，在六个数据集上平均超越了先前最先进方法5%。此外，我们还开发了参数量缩放的多个版本模型，即便在参数更少的情况下也取得了优于先前模型的性能。我们的代码和训练权重将很快发布。