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MMSeg:用于轻量级通用化的医疗图像细分的多尺度视觉mamba

Yayuan Mo¹, Yunhao Chen¹, Kaiwen Zhu¹

¹School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200000, China.

Journal of imaging informatics in medicine

|March 4, 2026

概括

MMSeg使用Vision Mamba提供高效的医疗图像细分,改善临床应用的跨域概括和计算性能.

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:

医疗图像细分对于计算机辅助诊断和治疗规划至关重要.
现有的方法面临跨领域概括和高计算成本的挑战.
细分任何模型 (SAM) 虽然强大,但其二次复杂性阻碍了临床使用.

研究的目的:

开发一个轻量级和高效的医疗图像细分框架.
为了提高跨领域的概括性和减少计算复杂性.
改善AI在医学成像中的临床部署.

主要方法:

建议使用Vision Mamba架构的MMSeg框架.
推出了多尺度轻量级Mamba编码器 (MLME),用于高效的多尺度特征处理.
开发了自动域匹配解码器 (ADMD),用于动态域对齐.
集成的特征蒸模块 (FDM) 用于使用少数镜头学习加速融合.

主要成果:

与最先进的方法相比,MMSeg在各种医疗数据集上表现出卓越的性能.
该框架在跨领域的概括方面取得了显著的改进.
MMSeg保持了高计算效率,适合资源有限的环境.

结论:

MMSeg为医疗图像细分提供了高效和有效的解决方案.
拟议的框架解决了现有方法在概括和计算负载方面的局限性.
在医疗图像分析中,MMSeg显示出实际临床部署的巨大潜力.

关键词:

域名通用化域名通用化轻量级的建筑轻量级的建筑.医疗图像细分医疗图像细分愿景愿景孟巴孟巴

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