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Updated: Jun 23, 2025

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使用机器和深度学习模型从血液涂抹图像中对白细胞 (白细胞) 的分类:全球范围审查

Rabia Asghar¹, Sanjay Kumar², Arslan Shaukat²

¹Spatiotemporal Environmental Epidemiology Research (STEER) Group, Technological University Dublin, Dublin, Ireland.

|June 17, 2024

概括

这篇评论强调了从机器学习到深度学习的转变,用于医学成像中的白细胞分类. 深度学习模型,特别是CNN,在较大的数据集中显示出卓越的准确性,解决了关键的知识差距.

科学领域:

医学成像分析医学成像分析
计算病理学计算病理学
人工智能在医学中的应用

背景情况:

机器学习 (ML) 和深度学习 (DL) 越来越多地用于医学图像分析,包括癌症和疾病诊断.
关于在血液涂抹图像中对白细胞 (WBC) 分类的ML和DL技术的审查和比较,存在一个知识差距.

研究的目的:

为了全面识别,探索和对比ML和DL方法用于WBC分类.
确定该领域最广泛使用和表现最佳的技术.

主要方法:

制定并实施了一项正式的审查协议.
在2006年1月至2023年5月期间发表的136项初级研究被系统地识别和分析.
审查的重点是使用ML和DL进行WBC分类的技术,性能和趋势.

主要成果:

从传统的ML转向DL,特别是卷积神经网络 (CNN),观察到一个显著的转变,54.4%的研究使用CNN.
传统的ML模型实现了高精度 (高达99%),但性能在较小的数据集下降.
随着较大数据集的使用,DL模型表现出更好的性能和更高的准确性.

结论:

深度学习,特别是CNN,正在成为WBC分类中的统治者,特别是在广泛的数据集中.
数据增强是应对数据集可用性挑战的潜在解决方案.
跨学科培训和对医学AI的认识对于未来的进步至关重要.

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