深度平衡展开学习用于光学分子成像中的噪声估计和删除
Lidan Fu1, Lingbing Li2, Binchun Lu3
1CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
概括
这项研究介绍了Deep Equilibrium Unfolding Mamba (DEQ-UMamba),这是一种用于消除光学分子图像的新型深度学习模型. DEQ-UMamba有效地抑制噪音,提高图像质量,实现更安全,更具成本效益的临床成像.
科学领域:
- 医疗成像医学成像
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 临床光学分子成像需要高率和低激发剂量,增加对噪声的敏感性.
- 严重的噪音会降低图像质量,需要有效的无噪技术.
- 由于忽视了物理模型,目前的深度学习方法缺乏可解释性和最佳性能.
研究的目的:
- 开发一种可解释和有效的深度学习模型,用于消除临床光学分子图像的噪音.
- 在性能和可解释性方面解决现有的拒绝方法的局限性.
- 改善分子成像中获取成本和图像质量之间的权衡.
主要方法:
- 引入了一个端到端的模型驱动的深度平衡展开的Mamba (DEQ-UMamba).
- 集成的近距离梯度下降与学习的空间频率特征,用于噪声解.
- 通过差异化平衡点来确保培训稳定性,采用隐式映射.
主要成果:
- 在光图像中,DEQ-UMamba有效地估计和抑制噪声.
- 该模型通过将网络模块与优化操作对齐来证明了清晰的结构解释性.
- 与最先进的方法相比,在较少参数的临床和体内数据集上获得了优异的性能.
结论:
- DEQ-UMamba提供了一种稳定,可解释和高性能解决方案,用于消除光学分子图像的模糊.
- 该方法促进了成本效益高,高质量的临床分子成像.
- 这种方法通过克服当前深度学习技术的局限性来推动该领域的进步.
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