Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

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.

Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study

“…This bibliometric study analyzed documents related to AI in oncol-ogy and published between January 1, 2012 and January 1, 2022. The search strategy was developed based on previous work [27,35,[37][38][39][40] involving bibliometric descriptive analyses and science mappings of the literature related to AI in oncology. A brief overview of these previous studies and the full search strategy is described in the Supplementary Material and Supplementary Table 1.…”

“…For example, there was only a small number of articles pertaining to AI and pediatric oncology up until 2017, but the number increased sharply thereafter [40]. Moreover, researchers in the domain of DL and breast cancer image classification began working collaboratively in 2017 and have since published their work, with this domain continuing to grow [38]. Therefore, it can be inferred that growing global collaboration after 2017 facilitated the dissemination of AI awareness in cancer research, leading to dramatically increased productivity since that year.…”

“…Other studies that use bibliometric approaches combined with network mappings have been limited to providing an overview of the different branches of AI applied to different cancer types, such as DL applied to the classification of breast cancer images [38], ML-based breast cancer prediction [39], and AI for prostate cancer [27] and pediatric oncology [40].…”

miRNA-448 Regulates the Development of Glioblastoma (GBM) by Regulating Rho-Associated Protein Kinase 1

“…In general, the mean survival time of GBM patients after surgery, chemotherapy, and radiotherapy is approximately 14 months, and most GBM patients have a poor prognosis [ 3 ]. Today, targeted therapy and biological therapy have become the new direction of GBM treatment [ 4 , 5 ] and also help to prolong the survival of patients. Therefore, exploring new therapeutic targets is of great significance for GBM patients.…”

Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021]

“…Recent advances in deep learning have emerged in several applications, ranging from natural language to vision processing Zou et al, (2019 ). Bibliometric approaches have generated a considerable impact on the deep learning research field, such as deep learning networks in identifying medical images and histopathology images for breast cancer classification ( Khairi et al, 2021 ; Wang et al, 2022 ). However, gaps exist for deep learning in genetics research, and there is a dearth of information on associated bibliometric development trends.…”