脑卒中损伤细分和深度学习:综合性审查
Mishaim Malik1, Benjamin Chong1,2,3, Justin Fernandez1,3,4
1Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand.
Bioengineering (Basel, Switzerland)
|January 22, 2024
概括
深度学习模型显著改善了中风病变的细分,有助于诊断和治疗. 本综述探讨了各种模型和预处理影响,以更好地分析中风病变.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在全球范围内,中风影响着数以百万计的人,导致显著的运动,言语,认知和情绪障碍.
- 精确的中风病变细分对于理解解剖信息和患者预后至关重要.
- 传统的手动细分方法正在被先进的计算技术所超越.
研究的目的:
- 审查用于中风病变细分的最先进的深度学习模型.
- 分析各种预处理技术对模型性能的影响.
- 引导未来的研究开发更有效的中风病变细分工具.
主要方法:
- 基于深度学习的中风病变细分模型的综合文献综述.
- 分析评估预处理技术对细分精度的影响的研究.
- 综合发现,以提供当前方法的概述.
主要成果:
- 深度学习模型在自动化中风病变细分方面表现出高效.
- 预处理技术显著影响这些模型的性能和稳定性.
- 各种深度学习架构正在被应用,不同程度的成功.
结论:
- 深度学习为中风病变细分提供了强大的方法,提高了效率和准确性.
- 优化预处理步骤对于最大化深度学习模型的性能至关重要.
- 需要进一步的研究来开发更强大的和可通用的模型用于临床应用.
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