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变压器的优势及其用于医疗图像分割的应用:一项调查调查
Qiumei Pu1, Zuoxin Xi1,2, Shuai Yin1
1School of Information Engineering, Minzu University of China, Beijing, 100081, China.
Biomedical engineering online
|February 3, 2024
概括
变压器在医疗图像细分方面表现有前途,但有限的数据需要将它们与卷积神经网络相结合. 预训练模型可以帮助克服纯变压器方法的数据限制.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 卷积神经网络 (CNN) 主导医疗图像细分.
- 在自然语言处理和图像分类方面取得成功的变压器模型越来越多地应用于医学成像.
研究的目的:
- 对医疗图像细分的基于变压器的模型进行审查.
- 在这个领域分析变压器与CNN的集成.
主要方法:
- 自2018年以来的论文的系统文献综述来自Web of Science.
- 在医学图像细分中分析变压器和CNN架构.
主要成果:
- 解释编解码器和变压器模块的构造.
- 基于变压器的医疗图像细分模型的概述.
- 概述常见的评估指标和数据集.
结论:
- 变压器模型显示出潜力,但由于医疗数据的可用性而受到限制.
- 结合变压器和CNN的混合型号是常见的.
- 预训可以减轻纯变压器模型的数据限制.
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