Abstract：
BACKGROUND: In the last decade, long-tail learning has become a popular research focus in deep learning applications in medicine. However, no scientometric reports have provided a systematic overview of this scientific field. We utilized bibliometric techniques to identify and analyze the literature on long-tailed learning in deep learning applications in medicine and investigate research trends, core authors, and core journals. We expanded our understanding of the primary components and principal methodologies of long-tail learning research in the medical field.
METHODS: Web of Science was utilized to collect all articles on long-tailed learning in medicine published until December 2023. The suitability of all retrieved titles and abstracts was evaluated. For bibliometric analysis, all numerical data were extracted. CiteSpace was used to create clustered and visual knowledge graphs based on keywords.
RESULTS: A total of 579 articles met the evaluation criteria. Over the last decade, the annual number of publications and citation frequency both showed significant growth, following a power-law and exponential trend, respectively. Noteworthy contributors to this field include Husanbir Singh Pannu, Fadi Thabtah, and Talha Mahboob Alam, while leading journals such as IEEE ACCESS, COMPUTERS IN BIOLOGY AND MEDICINE, IEEE TRANSACTIONS ON MEDICAL IMAGING, and COMPUTERIZED MEDICAL IMAGING AND GRAPHICS have emerged as pivotal platforms for disseminating research in this area. The core of long-tailed learning research within the medical domain is encapsulated in six principal themes: deep learning for imbalanced data, model optimization, neural networks in image analysis, data imbalance in health records, CNN in diagnostics and risk assessment, and genetic information in disease mechanisms.
CONCLUSIONS: This study summarizes recent advancements in applying long-tail learning to deep learning in medicine through bibliometric analysis and visual knowledge graphs. It explains new trends, sources, core authors, journals, and research hotspots. Although this field has shown great promise in medical deep learning research, our findings will provide pertinent and valuable insights for future research and clinical practice.
METHODS: Web of Science was utilized to collect all articles on long-tailed learning in medicine published until December 2023. The suitability of all retrieved titles and abstracts was evaluated. For bibliometric analysis, all numerical data were extracted. CiteSpace was used to create clustered and visual knowledge graphs based on keywords.
RESULTS: A total of 579 articles met the evaluation criteria. Over the last decade, the annual number of publications and citation frequency both showed significant growth, following a power-law and exponential trend, respectively. Noteworthy contributors to this field include Husanbir Singh Pannu, Fadi Thabtah, and Talha Mahboob Alam, while leading journals such as IEEE ACCESS, COMPUTERS IN BIOLOGY AND MEDICINE, IEEE TRANSACTIONS ON MEDICAL IMAGING, and COMPUTERIZED MEDICAL IMAGING AND GRAPHICS have emerged as pivotal platforms for disseminating research in this area. The core of long-tailed learning research within the medical domain is encapsulated in six principal themes: deep learning for imbalanced data, model optimization, neural networks in image analysis, data imbalance in health records, CNN in diagnostics and risk assessment, and genetic information in disease mechanisms.
CONCLUSIONS: This study summarizes recent advancements in applying long-tail learning to deep learning in medicine through bibliometric analysis and visual knowledge graphs. It explains new trends, sources, core authors, journals, and research hotspots. Although this field has shown great promise in medical deep learning research, our findings will provide pertinent and valuable insights for future research and clinical practice.
摘要：
背景：在过去的十年中，长尾学习已成为深度学习在医学中应用的热门研究热点。然而,没有科学计量学报告对这一科学领域提供了系统的概述。我们利用文献计量技术来识别和分析长尾学习在医学深度学习应用中的文献，并调查研究趋势。核心作者,和核心期刊。我们扩展了对医学领域长尾学习研究的主要组成部分和主要方法的理解。
方法：WebofScience被用来收集直到2023年12月出版的所有关于医学长尾学习的文章。评估了所有检索到的标题和摘要的适用性。对于文献计量分析，提取了所有数值数据。CiteSpace用于基于关键字创建集群和视觉知识图。
结果：共579篇文章符合评价标准。在过去的十年里，年度出版物数量和引用频率均显示出显着增长，遵循幂律和指数趋势，分别。这一领域值得注意的贡献者包括HusanbirSinghPannu，FadiThabtah,还有TalhaMahboobAlam,在IEEEACCESS等领先期刊上，生物学和医学计算机,IEEE医学成像事务，计算机医学图像和图形已成为传播该领域研究的关键平台。医学领域长尾学习研究的核心包含六个主要主题：不平衡数据的深度学习，模型优化，图像分析中的神经网络，健康记录中的数据不平衡，CNN在诊断和风险评估中，和疾病机制中的遗传信息。
结论：本研究通过文献计量分析和可视化知识图总结了将长尾学习应用于医学深度学习的最新进展。它解释了新趋势，来源,核心作者,期刊,和研究热点。尽管这一领域在医学深度学习研究中显示出巨大的前景，我们的研究结果将为未来的研究和临床实践提供有价值的见解.
方法：WebofScience被用来收集直到2023年12月出版的所有关于医学长尾学习的文章。评估了所有检索到的标题和摘要的适用性。对于文献计量分析，提取了所有数值数据。CiteSpace用于基于关键字创建集群和视觉知识图。
结果：共579篇文章符合评价标准。在过去的十年里，年度出版物数量和引用频率均显示出显着增长，遵循幂律和指数趋势，分别。这一领域值得注意的贡献者包括HusanbirSinghPannu，FadiThabtah,还有TalhaMahboobAlam,在IEEEACCESS等领先期刊上，生物学和医学计算机,IEEE医学成像事务，计算机医学图像和图形已成为传播该领域研究的关键平台。医学领域长尾学习研究的核心包含六个主要主题：不平衡数据的深度学习，模型优化，图像分析中的神经网络，健康记录中的数据不平衡，CNN在诊断和风险评估中，和疾病机制中的遗传信息。
结论：本研究通过文献计量分析和可视化知识图总结了将长尾学习应用于医学深度学习的最新进展。它解释了新趋势，来源,核心作者,期刊,和研究热点。尽管这一领域在医学深度学习研究中显示出巨大的前景，我们的研究结果将为未来的研究和临床实践提供有价值的见解.
