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TUMamba:一种基于Mamba和U-Net的新语言细分方法

Fan Jiang¹, Yanmei Zhong², Simin Yang³

¹Rehabilitation Medicine Department, The first Affiliated Hospital of Chongqing Medical University, Chongqing, China.

|November 1, 2024

概括

本研究介绍了一种新的舌头细分模型,将Mamba和U-Net结合起来,以提高复杂环境中的准确性. 图曼巴模型增强了特征提取和过,优于现有的方法.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

当前的舌头细分方法面临着全球特征提取和复杂环境中的选择性过的挑战.
类似于舌头的背景对象可以显著降低细分效率和准确性.

研究的目的:

在复杂的环境中提出一种新的舌头细分模型.
通过结合Mamba和U-Net来提高细分精度和效率.

主要方法:

将Mamba关注模块和多阶段功能融合模块集成到U-Net骨干中.
马巴注意模块在U-Net的跳过连接上连接空间和通道注意,用于选择性特征地图过.
多级特征融合模块集成了不同阶段的特征地图,以提高性能.

主要成果:

与最先进的模型相比,拟议的模型在平均交叉与联合 (mIoU) 中实现了1.17%的改进.
废弃实验证实,每一个拟议的模块都有助于提高细分效率.

结论:

基于U-Net和Mamba (TUMamba) 的Tongue细分模型有效地提取全球和本地特征.
该模型在复杂环境中的舌头细分任务中表现出色,证明了其实际价值.

关键词:

聪明的舌头细分方式马姆巴·马姆巴是什么意思这就是U-Net.多个阶段的功能融合.

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