[论文解读] ProMamba: Prompt-Mamba for polyp segmentation

Jianhao Xie, Ruofan Liao+4 more|arXiv (Cornell University)|Mar 20, 2024

Natural Language Processing Techniques被引用 8

一句话总结

ProMamba 使用 Vision-Mamba 和提示技术进行息肉分割，实现强泛化的跨数据集，以及在六个数据集上平均提升约 5%。

ABSTRACT

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.

研究动机与目标

• 推动准确的息肉分割，以辅助基于结肠镜的临床决策。
• 解决数据集之间息肉形状、颜色差异及边界不清的问题。
• 提升对训练数据之外的未见数据集的泛化能力。

提出的方法

• 基于 Vision-Mamba 架构引入用于息肉分割的 Prompt-Mamba。
• 将提示技术融入，以引导分割并改进边界描绘。
• 在未见数据集上展示出比以往方法更高的准确性，同时使用可扩展的参数量。
• 报告该模型在六个数据集上平均领先现有最佳方法约 5%。

实验结果

研究问题

• RQ1 结合提示的 Vision-Mamba 能否在未见数据集上提高息肉分割的准确性？
• RQ2 Prompt-Mamba 在多样化息肉数据集上的泛化能力是否优于此前方法？
• RQ3 ProMamba 的不同参数量如何影响性能和效率？
• RQ4 这是 Vision-Mamba 与提示首次应用于息肉分割吗？并且有哪些经验收益？

主要发现

• 该模型在未见数据集上实现了更高的分割准确性，表明具有强泛化能力。
• ProMamba 在六个数据集上平均超越之前的最先进方法约 5%。
• 多种具备不同参数量的模型变体在参数更少的情况下仍实现更好的性能。
• Vision-Mamba 首次应用于息肉分割，且首次在息肉分割模型中使用提示。
• 计划发布代码和训练权重。