视觉变压器在图像分析中的应用
Yuwei Yin1,2, Zhixian Tang3, Huachun Weng4,5
1The College of Health Sciences and Engineering, University of Shanghai for Science and Technology, 516 Jungong Highway, Yangpu Area, Shanghai, 200093, China.
Biomedical engineering online
|March 4, 2024
概括
深度自我注意网络 (变压器) 正通过有效处理远距离依赖来彻底改变图像分析. 本综述探讨了它们在细分,分类和检测方面的应用,并提供了对脏图像处理的未来趋势的见解.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 腎臟病學 (nephrology) 是一種醫學.
背景情况:
- 深度自我注意网络 (转换器),最初用于自然语言处理,擅长捕获远距离依赖.
- 变压器补充卷积神经网络 (CNN),导致它们在医学图像分析中的应用越来越多,特别是用于脏成像.
- 脏图像处理是一个快速发展的研究领域,具有人工智能驱动的进步的巨大潜力.
研究的目的:
- 概述变压器网络模型的特征.
- 总结基于变压器的模型在各种脏图像处理任务中的应用.
- 为了比较基于变压器的方法与传统的基于CNN的算法在图像分析.
主要方法:
- 对变压器网络架构的审查及其适用于医学成像的适用性.
- 综合当前关于变压器在图像细分,分类,检测,电子病历和决策系统中的应用研究.
- 对变压器和CNN在脏图像处理中的性能进行比较分析.
主要成果:
- 变压器在图像细分,分类和检测任务中显示出显著的潜力.
- 变形金刚与CNN的互补性质为复杂的图像分析提供了增强的能力.
- 分析突出了变压器模型与CNN相比在这个领域的优点和缺点.
结论:
- 变压器模型代表了脏图像处理的前沿,为各种分析挑战提供了新的解决方案.
- 预计变压器的整合将推动图像分析的创新,提高诊断准确性和治疗规划.
- 本综述为研究人员和从业人员探索人工智能在脏成像中的未来发展提供了有价值的参考.
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