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由数据引力和弱相关性指导的医疗超光谱图像的无监督带选择

Chenglong Zhang¹, Zhimin Zhang¹, Dexin Yu²

¹School of Control Science and Engineering, Shandong University, Jinan 250061, China.

Computer methods and programs in biomedicine

|July 28, 2023

概括

一种新的方法,数据引力和基于弱相关的排名 (DGWCR),有效地减少了医疗超谱图像 (MHSI) 的维度,而不会扭曲数据. 这种技术可以提高临床应用的分类准确性.

科学领域:

医学成像医学成像
数据科学是数据科学.

背景情况:

医学高光谱图像 (MHSI) 提供无辐射的非侵入性检查.
高维度的MHSIs导致"维度的诅咒",使处理和存储复杂化.
现有的缩小维度的方法往往会扭曲MHSI数据.

研究的目的:

为MHSI开发一种光谱维度减小方法,以保持数据结构.
解决MHSI中的"维度诅咒",以改善临床应用.
减少与MHSI相关的数据复杂性和存储成本.

主要方法:

建议基于数据引力和弱相关性排名 (DGWCR) 进行噪声频段去除和信号频段聚类.
使用连接中心演变 (CCE) 算法进行频段聚类和复合力进行代表性频段选择.
采用包含的矩阵和S型策略进行全球频段排名.

主要成果:

与原始数据相比,DGWCR方法在缩小尺寸的MHSI中实现了更高的分类准确性.
DGWCR选择的频段具有最小的冗余和最大的歧视,超过深度学习策略.
实验验证实了DGWCR在三个人类大脑MHSI上的有效性.

结论:

DGWCR为MHSI中的频段选择提供了一个有效的解决方案,减轻了"维度的诅咒".
由于其卓越的性能,该方法显示出临床应用的巨大潜力.
DGWCR的性能优于现有的频段选择技术,突出其临床实用性.

关键词:

带的选择带的选择数据引力数据引力人类大脑人类大脑医学高光谱图像医学高光谱图像

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