基于病理图像预测癌症分子变化的深度学习应用:通过CiteSpace进行文献分析
Yu Xiaojian1,2,3, Qu Zhanbo1,2,3, Chu Jian1,2,3
1Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China.
Journal of cancer research and clinical oncology
|October 18, 2024
概括
人工智能 (AI) 正在彻底改变分子病理学的图像识别. 深度学习有望实现更快,更便宜的检测,但数据质量和可解释性需要进一步研究.
科学领域:
- 分子病理学分子病理学
- 人工智能的人工智能
- 图像识别 图像识别
背景情况:
- 人工智能图像识别方面的进步正在改变分子病理学.
- 这个领域正在进入一个新的研究和开发时代.
研究的目的:
- 确定分子病理学图像识别的关键研究领域和趋势.
- 分析这个科学领域的演变.
主要方法:
- 从科学网络 (2010-2023) 的110篇文章的图书统计分析.
- 使用CiteSpace进行可视化,以映射关键字,引文和趋势.
- 专注于确定领先的机构,国家和作者.
主要成果:
- 出版数量显示每年稳步增长.
- 美国在出版物方面领先 (38),其次是德国 (50%的顶级机构).
- 深度学习在2019年成为一个关键术语;微卫星在结直肠癌中的不稳定性识别是最近的一个重点.
结论:
- 深度学习 (DL) 为有效的分子病理检测提供了一个潜在的解决方案.
- 进一步的研究对于应对数据标准化,模型解释性和基础设施方面的挑战至关重要.
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