CDDnet:通过本地和全球信息对齐,为低剂量CT图像提供跨领域的拒绝网络
Jiaxin Huang1, Kecheng Chen2, Yazhou Ren3
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Computers in biology and medicine
|July 9, 2023
概括
这项研究引入了一种新的跨域消噪网络 (CDDnet),以解决低剂量CT (LDCT) 成像中的域转移问题. CDDnet有效地消除了不同领域的图像,优于现有的方法.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 域移动在跨域低剂量CT (LDCT) 图像消噪方面构成了重大挑战.
- 获取多样化的医疗图像数据集往往受隐私问题和数据可用性限制.
- 现有的方法很难在不同的CT获取领域中有效地泛化.
研究的目的:
- 为有效的LDCT形象宣传提出一个新的跨领域宣传网络 (CDDnet).
- 通过将CT图像的本地和全球信息结合起来,解决域移动问题.
- 改善LDCT在跨领域场景中的表现.
主要方法:
- 开发了一个跨领域的拒绝网络 (CDDnet),集成了本地和全球信息处理.
- 引入了局部信息对齐模块,以规范目标和源补丁之间的特征相似性.
- 使用自动编码器来学习潜在的相关性,以从全球角度对准语义结构.
主要成果:
- CDDnet有效地缓解了LDCT图像消噪中的域位移问题.
- 与现有的深度学习和域名适应方法相比,拟议的网络表现出卓越的性能.
- 实验结果验证了CDDnet在跨领域的拒绝任务中的有效性.
结论:
- 该CDDnet提供了一个强大的解决方案,用于跨域LDCT图像denoising.
- 整合本地和全球信息对齐对于减轻域名转移至关重要.
- CDDnet提供了一种有前途的方法,可以在各种采集环境中提高医疗图像质量.
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