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基于组织病理学图像的甲状腺瘤人工智能驱动分类趋势 - - 一个系统性审查
Haitham Kussaibi1, Noor Alsafwani1
1Department of Pathology, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia.
概括
深度学习精确地从组织病理学图像中分类甲状腺结节,在大多数研究中达到90%以上的准确性. 需要进一步的研究来解决诸如临床使用的小型数据集等局限性问题.
科学领域:
- 病理学 病理学 病理学
- 计算机科学 计算机科学
- 在瘤学瘤学.
背景情况:
- 甲状腺结节的准确诊断在很大程度上依赖于组织病理学.
- 人工智能 (AI),特别是深度学习,在提高诊断准确性方面表现有前途.
- 本文重点介绍了使用基因病理学进行甲状腺结节分类的深度学习应用.
研究的目的:
- 系统地审查和分析最近的深度学习方法,以基于基因病理图像的甲状腺结节分类.
- 为了评估这些AI模型的性能.
- 确定当前的局限性,并建议未来的研究方向.
主要方法:
- 在过去5年内发表的同行评审英语文学的系统审查.
- 使用深度学习和甲状腺组织病理学相关的关键词搜索PubMed数据库.
- 包括应用深度学习来分类甲状腺组织病理学图像的研究.
主要成果:
- 十一项研究符合纳入标准,所有研究都使用卷积神经网络 (CNN).
- 大多数研究都集中在基本瘤亚型化上,有三项研究预测了与瘤相关的突变.
- 报告的准确性在77%至100%之间,大多数超过90%.
结论:
- 深度学习有效地识别了组织病理学图像中的形态模式,提高了甲状腺结节分类的准确性.
- 限制包括小样本大小,主观注释和有限的数据集多样性.
- 未来的研究应该专注于更大,多样化的数据集,模型优化和临床验证,以便在现实世界中应用.
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