基于变压器的神经成像方法:对它们在分类和回归任务中的作用进行了深入的审查
Xinyu Zhu1, Shen Sun1, Lan Lin1
1Department of Biomedical Engineering, 12496 College of Chemistry and Life Sciences, Beijing University of Technology , Beijing, 100124, China.
Reviews in the neurosciences
|September 27, 2024
概括
变压器模型是强大的深度学习工具,彻底改变了神经成像. 本综述探讨了它们在脑图像分类和回归中的应用,强调了当前的进展和未来的潜力.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 医疗成像医学成像
背景情况:
- 深度学习 (DL) 模型,特别是变压器,在神经成像中越来越多地被使用.
- 变压器为分析复杂的大脑数据提供了先进的功能.
研究的目的:
- 在神经成像中提供变压器应用的全面审查.
- 阐明变压器模型在这个领域的当前状态和进展.
主要方法:
- 在神经成像中对变压器模型的最新研究的文献调查.
- 对分类和回归任务的方法和实验结果的分析.
主要成果:
- 变压器在神经图像分类任务中表现出色.
- 变压器在神经图像回归任务中显示出显著且不断增长的潜力.
结论:
- 变压器模型是神经成像分析的一个关键进步.
- 未来的研究应该解决当前的挑战,并探索变压器应用的新轨迹.
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