Medical long-tailed learning for imbalanced data: Bibliometric analysis

Authors:
raw_author_name: Zheng Wu
raw_affiliation_string: School of Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China
raw_author_name: Kehua Guo
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Entao Luo
raw_affiliation_string: School of Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China
raw_author_name: Tian Wang
raw_affiliation_string: BNU-UIC Institute of Artificial Intelligence and Future Networks, Beijing Normal University (BNU Zhuhai), Zhuhai, China
raw_author_name: Shoujin Wang
raw_affiliation_string: Data Science Institute, University of Technology Sydney, Sydney, Australia
raw_author_name: Yi Yang
raw_affiliation_string: Department of Computer Science, Northeastern Illinois University, Chicago, IL 60625, USA
raw_author_name: Xiangyuan Zhu
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Rui Ding
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Abstract: 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.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.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.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.