对不平衡数据进行医学长尾学习:图书统计学分析.
Zheng Wu1, Kehua Guo2, Entao Luo1
1School of Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China.
Computer methods and programs in biomedicine
|March 7, 2024
概括
这篇关于医学深度学习中长尾学习的文献分析揭示了显著的增长和关键研究主题. 结果为未来的医学AI研究提供了对趋势,作者和期刊的见解.
科学领域:
- 医疗人工智能 医疗人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
- 长尾学习 长尾学习
背景情况:
- 长尾学习是医学深度学习中日益关注的焦点.
- 缺乏使用文献计量技术对这一领域的系统概述.
- 本研究提供了对科学文献的全面分析.
研究的目的:
- 系统地分析医学深度学习应用中长尾学习的文献.
- 确定研究趋势,核心作者和该领域的关键期刊.
- 阐明医学中长尾学习研究的主要组成部分和方法.
主要方法:
- 从Web of Science对579篇发表到2023年12月的文章进行了图书统计分析.
- 评价标题和摘要的适用性.
- 使用基于关键字的CiteSpace创建视觉知识图表.
主要成果:
- 在过去的十年中,出版物和引用的数量显著增长.
- 确定主要贡献者 (Husanbir Singh Pannu,Fadi Thabtah,Talha Mahboob Alam) 和期刊 (IEEE ACCESS,生物学和医学中的计算机). 这些期刊的编辑和出版商.
- 六个核心研究主题:数据不平衡,模型优化,图像分析中的神经网络,健康记录不平衡,诊断中的CNN和疾病机制中的遗传信息.
结论:
- 图书统计分析和视觉知识图表总结了医学深度学习的长尾学习的进展.
- 该研究强调了新的趋势,来源,作者,期刊和研究热点.
- 这些发现为未来的医学深度学习研究和临床实践提供了宝贵的见解.
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