ArticleHealthcare (Basel, Switzerland)2021
Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 40 citations in OpenAlex.
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- Evolution of artificial intelligence in breast cancer pathology: a bibliometric analysis.Discover oncology · 2026Article
- Bibliometric analysis of research on artificial İntelligence applications in breast cancer diagnosis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2026Article
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E-stained images for ovarian cancer prognosis.Journal of translational medicine · 2025Article
- Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning.Scientific reports · 2024Article
- Research Trends of Artificial Intelligence in Lung Cancer: A Combined Approach of Analysis With Latent Dirichlet Allocation and HJ-Biplot Statistical Methods.Pulmonary medicine · 2024Article
- Nontoxic Fluorescent Nanoprobes for Multiplexed Detection and 3D Imaging of Tumor Markers in Breast Cancer.Pharmaceutics · 2023Review
- Thirty-year survey of bibliometrics used in the research literature of pain: Analysis, evolution, and pitfalls.Frontiers in pain research (Lausanne, Switzerland) · 2023Review
- Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis.International journal of environmental research and public health · 2022Article
- miRNA-448 Regulates the Development of Glioblastoma (GBM) by Regulating Rho-Associated Protein Kinase 1.Computational and mathematical methods in medicine · 2022Article
- Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000-2021].Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious work and can be misleading due to human error. Therefore, this study explored the research status and development trends of deep learning on breast cancer image classification using bibliometric analysis. Relevant works of literature were obtained from the Scopus database between 2014 and 2021. The VOSviewer and Bibliometrix tools were used for analysis through various visualization forms. This study is concerned with the annual publication trends, co-authorship networks among countries, authors, and scientific journals. The co-occurrence network of the authors' keywords was analyzed for potential future directions of the field. Authors started to contribute to publications in 2016, and the research domain has maintained its growth rate since. The United States and China have strong research collaboration strengths. Only a few studies use bibliometric analysis in this research area. This study provides a recent review on this fast-growing field to highlight status and trends using scientific visualization. It is hoped that the findings will assist researchers in identifying and exploring the potential emerging areas in the related field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.