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MambaMIM:先训练Mamba使用状态空间令牌插值及其应用于医疗图像细分的应用

Fenghe Tang¹, Bingkun Nian², Yingtai Li¹

¹School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, PR China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, PR China; Center for Medical Imaging, Robotics, and Analytic Computing & LEarning (MIRACLE), Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, 215123, PR China.

Medical image analysis

|May 10, 2025

概括

MambaMIM是一个新的掩面图像建模框架,通过利用因果状态空间属性来增强Mamba在3D医学成像方面的功能. 这种方法在医疗图像细分任务中实现了最先进的性能.

科学领域:

人工智能的人工智能
医疗成像医学成像
计算机视觉计算机视觉

背景情况:

像Mamba这样的国家空间模型在3D医学成像中显示出长序建模的前景.
现有的生成自主监督方法无法充分利用Mamba对远程依赖的潜力,因为它在掩面建模中忽视了因果性质.

研究的目的:

推出MambaMIM,一个通用的预培训框架,旨在释放Mamba在医学图像分析方面的能力.
为了增强Mamba在医疗成像任务中的多尺度和远程表示能力.

主要方法:

开发了MambaMIM,这是一个面具图像建模方法,用于选择性状态空间序列的新TOKen-Interpolation (TOKI) 策略.
实现了一个自下而上的3D混合掩盖策略,以便在整个架构中实现一致的掩盖.
预先训练的MambaMIM在一个大数据集上进行了6.8K CT 扫描.

主要成果:

在医学影像预训练中,MambaMIM显示出了显著的进步.
当应用到混合MedNeXt和Vision Mamba架构时,实现了最先进的细分性能.
通过八个公共基准验证了Mamba在医学影像预培训中的可行性和进步.

结论:

MambaMIM有效地解决了当前用于医学成像中的Mamba生成自我监督学习的局限性.
拟议的框架提高了Mamba在长序医疗视觉任务中的性能,特别是在细分方面.
这项研究强调了Mamba作为未来医学图像分析研究的强大工具的潜力.

关键词:

蒙面图像建模蒙面图像建模医学影像预培训培训医疗图像细分医疗图像细分国家空间令牌插值.

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