Artículo

Deep learning-based object detection algorithms in medical imaging: Systematic review

Resumen

Over the past decade, Deep Learning (DL) techniques have demonstrated remarkable advancements across various domains, driving their widespread adoption. Particularly in medical image analysis, DL received greater attention for tasks like image segmentation, object detection, and classification. This paper provides an overview of DL-based object recognition in medical images, exploring recent methods and emphasizing different imaging techniques and anatomical applications. Utilizing a meticulous quantitative and qualitative analysis following PRISMA guidelines, we examined publications based on citation rates to explore into the utilization of DL-based object detectors across imaging modalities and anatomical domains. Our findings reveal a consistent rise in the utilization of DL-based object detection models, indicating unexploited potential in medical image analysis. Predominantly within Medicine and Computer Science domains, research in this area is most active in the US, China, and Japan. Notably, DL-based object detection methods have gotten significant interest across diverse medical imaging modalities and anatomical domains. These methods have been applied to a range of techniques including CR scans, pathology images, and endoscopic imaging, showcasing their adaptability. Moreover, diverse anatomical applications, particularly in digital pathology and microscopy, have been explored. The analysis underscores the presence of varied datasets, often with significant discrepancies in size, with a notable percentage being labeled as private or internal, and with prospective studies in this field remaining scarce. Our review of existing trends in DL-based object detection in medical images offers insights for future research directions. The continuous evolution of DL algorithms highlighted in the literature underscores the dynamic nature of this field, emphasizing the need for ongoing research and fitted optimization for specific applications.
Tao, Sha-Sha (55923358700); Tang, Jian (59164595100); Yang, Xiao-Ke (55428143500); Fang, Xi (57651754300); Luo, Qing-Qing (59164728100); Xu, Yi-Qing (58506838600); Ge, Man (58507954100); Ye, Fan (59165379100); Wang, Peng (56982050800); Pan, Hai-Feng (58516287500)
Two Decades of Publications in Journals Dedicated to Autoimmunity: A Bibliometric Analysis of the Autoimmunity Field from 2004 to 2023
2024
10.1007/s10238-024-01369-1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195479682&doi=10.1007%2fs10238-024-01369-1&partnerID=40&md5=d24655fd578888e63375207f1e94e3e9
Department of Epidemiology and Biostatistics, School of Public Health, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, Anhui, Hefei, 230032, China; Preventive Medicine Experimental Teaching Center, School of Public Health, Anhui Medical University, Anhui, Hefei, 230032, China; Department of Rheumatology and Immunology, The First Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230032, China; Department of Environmental Health, School of Public Health, Shanxi Medical University, Shanxi, TaiYuan, 030001, China; Teaching Center for Preventive Medicine,School of Public Health, Anhui Medical University, Anhui, Hefei, 230032, China
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