对抗制
医学
背景(考古学)
医学影像学
生成语法
领域(数学)
人工智能
计算机科学
数据科学
医学物理学
放射科
纯数学
生物
古生物学
数学
作者
Konstantinos Vrettos,Emmanouil Koltsakis,Aristeidis Zibis,Apostolos H. Karantanas,Michail E. Klontzas
标识
DOI:10.1016/j.ejrad.2024.111313
摘要
In recent years, the field of medical imaging has witnessed remarkable advancements, with innovative technologies which revolutionized the visualization and analysis of the human spine. Among the groundbreaking developments in medical imaging, Generative Adversarial Networks (GANs) have emerged as a transformative tool, offering unprecedented possibilities in enhancing spinal imaging techniques and diagnostic outcomes. This review paper aims to provide a comprehensive overview of the use of GANs in spinal imaging, and to emphasize their potential to improve the diagnosis and treatment of spine-related disorders. A specific review focusing on Generative Adversarial Networks (GANs) in the context of medical spine imaging is needed to provide a comprehensive and specialized analysis of the unique challenges, applications, and advancements within this specific domain, which might not be fully addressed in broader reviews covering GANs in general medical imaging. Such a review can offer insights into the tailored solutions and innovations that GANs bring to the field of spinal medical imaging.
科研通智能强力驱动
Strongly Powered by AbleSci AI