扩散模型用于医疗图像的破坏,重建和翻译
Wei Wang1, Jiayu Xia1, Gongning Luo2
1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, HIT Campus of University Town of Shenzhen, Taoyuan Street, Nanshan District, Shenzhen, 518055, Guangdong, China.
概括
扩散模型通过逐步消除噪音来产生高质量的图像来增强医学成像. 本综述涵盖了它们在破解,重建和翻译方面的应用,以改善诊断.
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:
- 扩散模型是学习逆转增加噪声过程的生成模型.
- 他们已经在一般图像生成和各种图像处理任务中取得了成功.
- 它们在医学成像中的应用正在出现,显示出提高诊断准确性和图像质量的潜力.
研究的目的:
- 提供医学图像分析中扩散模型的全面概述.
- 将扩散模型分类为分散的概率模型和基于分数的模型.
- 总结当前的应用,并讨论医学成像的未来方向.
主要方法:
- 综述扩散模型的理论基础和基本概念.
- 分类为无声扩散概率模型和基于分数的模型.
- 介绍相关数据集和评估指标.
主要成果:
- 扩散模型显示了医疗图像消毒,重建和翻译的巨大潜力.
- 对代表性文章的详细介绍强调了当前的应用.
- 讨论挑战和未来的研究途径.
结论:
- 扩散模型为推进医学图像分析提供了一个有希望的方法.
- 需要进一步的研究才能在临床环境中充分发挥其潜力.
- 持续开发可能会导致改进的诊断工具和患者的治疗结果.
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