使用机器视觉方法对肠道病原体图像分析进行全面的调查
Yujie Jing1, Chen Li1, Tianming Du1
1Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
Computers in biology and medicine
|September 11, 2023
概括
机器学习 (ML) 提高了结直肠癌 (CRC) 诊断使用组织病理学图像. 本综述涵盖了用于CRC图像分析的ML方法,提高了诊断效率和客观性.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 计算病理学计算病理学
背景情况:
- 结肠直肠癌 (CRC) 是全球和发达国家癌症死亡的主要原因.
- 组织病理学图像对于CRC诊断和治疗至关重要,提供丰富的表型信息.
- 传统的诊断方法在分析复杂的组织病理学数据时可能缺乏客观性和效率.
研究的目的:
- 综合审查最近的基于机器学习 (ML) 的方法,用于分析结肠直肠癌 (CRC) 中的肠道组织病理图像.
- 讨论常用的数据集和有关肠道组织病理学的相关医学知识.
- 探索ML方法在这个领域的应用前景.
主要方法:
- 传统的机器学习 (ML) 和深度学习 (DL) 方法的审查,应用于肠道组织病理学.
- 分析ML技术的细分,分类,检测和识别在他的病理图像.
- 在基础研究研究中讨论常用的数据集.
主要成果:
- 机器学习和深度学习方法在提高CRC的基因病理图像分析的客观性和效率方面显示出显著的希望.
- 目前正在开发各种ML技术,用于像图像分割,分类和检测这样的特定任务.
- 对当前的ML方法及其在分析肠道组织病理学中的表现的全面概述.
结论:
- 基于ML的计算机辅助诊断 (CAD) 系统对于推进肠道组织病理学的分析至关重要.
- 对ML方法的持续研究和开发将进一步提高结直肠癌的诊断准确性和效率.
- 审查的ML方法为CRC诊断和治疗的未来提供了有前途的应用.
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