儿科放射学中的生成对抗网络 (生成人工智能):系统性审查
1Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia.
Children (Basel, Switzerland)
|August 26, 2023
概括
生成对抗网络 (GAN) 在儿科放射学中显示出有前途的各种应用,如图像增强和诊断. 然而,目前研究的方法缺陷需要更强大的研究才能在临床采用.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 儿科成像学 儿科成像学
背景情况:
- 生成型人工智能 (AI),特别是生成式对抗性网络 (GAN),是医学成像领域快速发展的领域.
- 虽然在一般放射学中对GAN进行了审查,但对于儿科放射学缺乏专门的审查.
研究的目的:
- 系统地审查GAN在儿科放射学中的应用.
- 评估GAN模型的性能及其评估所使用的方法.
主要方法:
- 在电子数据库中进行了系统的文献搜索,截至2023年4月6日.
- 审查中包括了37篇相关论文.
主要成果:
- 在各种儿科成像模式 (MRI,X射线,CT,超声波,PET) 中应用GAN,用于包括图像翻译,细分,重建,质量评估,合成,数据增强和疾病诊断等任务.
- 大约80%的研究报告了GAN模型的性能优于其他方法,改进率从0.1%到158.6%不等.
- 在审查的研究中发现了方法方面的弱点.
结论:
- 在儿科放射学中,GANs提供了多样化的应用和潜在的性能益处.
- 当前研究的方法限制可能会阻碍临床采用和实现GAN在这个领域的全部潜力.
- 未来的研究应该专注于开发更强大的方法来解决发现的弱点.
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