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Medical long-tailed learning for imbalanced data: Bibliometric analysis.

Zheng Wu1, Kehua Guo2, Entao Luo1

  • 1School of Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China.

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|March 7, 2024
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Summary

This bibliometric analysis of long-tail learning in medical deep learning reveals significant growth and key research themes. Findings offer insights into trends, authors, and journals for future medical AI research.

Keywords:
Data imbalanceDeep learningLong-tailed learningMedical image recognitionMedical image segmentation

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Area of Science:

  • Medical Artificial Intelligence
  • Deep Learning
  • Long-tail Learning

Background:

  • Long-tail learning is a growing focus in medical deep learning.
  • A systematic overview of this field using bibliometric techniques was lacking.
  • This study provides a comprehensive analysis of the scientific literature.

Purpose of the Study:

  • To systematically analyze the literature on long-tail learning in deep learning applications in medicine.
  • To identify research trends, core authors, and key journals in the field.
  • To elucidate the primary components and methodologies of long-tail learning research in medicine.

Main Methods:

  • Bibliometric analysis of 579 articles published until December 2023 from Web of Science.
  • Evaluation of titles and abstracts for suitability.
  • Creation of visual knowledge graphs using CiteSpace based on keywords.

Main Results:

  • Significant growth in publications and citations over the past decade.
  • Identification of key contributors (Husanbir Singh Pannu, Fadi Thabtah, Talha Mahboob Alam) and journals (IEEE ACCESS, Computers in Biology and Medicine).
  • Six core research themes: imbalanced data, model optimization, neural networks in image analysis, health record imbalance, CNNs in diagnostics, and genetic information in disease mechanisms.

Conclusions:

  • Bibliometric analysis and visual knowledge graphs summarize advancements in long-tail learning for medical deep learning.
  • The study highlights new trends, sources, authors, journals, and research hotspots.
  • Findings offer valuable insights for future research and clinical practice in medical deep learning.