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医学成像中的扩散模型:一个全面的调查调查
Amirhossein Kazerouni1, Ehsan Khodapanah Aghdam2, Moein Heidari1
1School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
Medical image analysis
|June 9, 2023
概括
扩散模型是深度学习的强大生成工具,现在越来越多地应用于医学成像,用于重建和细分等任务. 这份调查提供了对它们的理论,应用和医疗领域未来方向的全面指南.
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:
- 无光散发模型是生成模型,在数据生成质量和覆盖率方面表现出色.
- 由于计算机视觉的进步,这些模型在医学成像中越来越受欢迎.
- 尽管计算成本高,但它们在医疗应用中的潜力是显著的.
研究的目的:
- 为医疗成像中的扩散模型提供全面的概述.
- 根据应用,模式,器官和算法系统地分类扩散模型.
- 引导研究人员通过这个领域不断增长的工作.
主要方法:
- 介绍扩散模型的理论基础及其三个主要框架:扩散概率模型,噪声条件下得分网络和随机微分方程.
- 医学中扩散模型的系统分类和多视角分类.
- 广泛的应用程序的审查,包括图像对图像的翻译,重建,注册,分类,细分,无声化,生成和异常检测.
主要成果:
- 扩散模型在各种医学成像任务中展示了广泛的适用性.
- 该调查对现有研究进行了分类,突出了关键算法及其在各种医疗环境中的使用.
- 讨论了实际的用例和局限性,以及未来的研究方向.
结论:
- 扩散模型为推进医学成像分析和生成提供了一个有希望的途径.
- 需要进一步的研究来解决局限性,并在临床环境中充分发挥其潜力.
- 提供了审查研究的开源存储库,以促进正在进行的研究和开发.
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