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用卷积神经网络进行分层的多层次动态超参数可变形图像注册.

Zhenyu Zhu1, Qianqian Li2, Ying Wei1,3

  • 1School of Control Science and Engineering, Shandong University, Jinan, People's Republic of China.

Physics in medicine and biology
|July 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于深度学习可变形图像注册 (DLDIR) 的动态超参数块,使得快速的超参数选择和提高准确性. 这种新的方法显著减少了培训时间,并提高了大脑和肺部数据集的注册性能.

关键词:
可变形图像的注册 变形图像的注册动态卷积的动态卷积有关特征统计的统计.层次化的多层架构结构.调节超参数的调节超参数

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 深度学习可变形图像注册 (DLDIR) 需要广泛的超参数调整,这耗时且资源密集.
  • 目前的DLDIR方法通常涉及许多独立的实验,用于规范化超参数选择.
  • 提高注册准确性和变形场规律性是DLDIR的关键挑战.

研究的目的:

  • 开发一种DLDIR方法,允许在推理过程中进行单次训练和快速规范化超参数选择.
  • 为了提高注册准确性和变形场规律性.
  • 为了降低与DLDIR中超参数调整相关的计算成本.

主要方法:

  • 提出了一种新的动态超参数块,包括分布式映射网络,动态卷积,注意力特征提取和实例规范化.
  • 将编码输入特征和规范化超参数转化为可学习的特征变量和动态卷积参数.
  • 为动态超参数块实施了层次化的多层架构,取代了单层剩余块.

主要成果:

  • 降低了OASIS数据集上的折叠的百分比 (gadgadJφgad0) 降低了28.01%和9.78%,与LapIRN和CIR相比,Dice相似度系数提高了1.17%.
  • 在DIR-Lab数据集上减少了10.00%和5.70%的折叠,与LapIRN和CIR相比,目标注册错误减少了10.84%和10.05%.
  • 与最先进的方法相比,证明了减少训练时间和更高的注册准确性和变形流性.

结论:

  • 拟议的方法使得在推断过程中对任意超参数的快速注册变形场生成.
  • 在基准数据集上,在注册准确性和变形顺性方面实现了最先进的性能.
  • 与固定超参数的传统DLDIR方法相比,在培训时间上提供了显著的减少.