对光学连贯性进行域识别的少数镜头学习 断层扫描 降低噪音
1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Journal of imaging
|November 24, 2023
概括
本研究介绍了光学连贯性断层扫描 (OCT) 降噪的快速几次拍摄学习框架. 它可以使用最小的数据进行准确的脱落,提高深度学习模型的适应性,用于医学成像.
科学领域:
- 医疗成像医学成像
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算成像技术的成像
背景情况:
- 斑点噪声在医学成像中是一个持续的挑战,特别是在光学连贯断层扫描 (OCT) 中.
- 深度学习方法有先进的降噪能力,但监督模型却难以应对由不同的获取参数和生物组织引起的域移位.
- 现有的深度神经网络 (DNN) 容易受到采样空间,分辨率和对比度的变化的影响,从而降低了未见数据的性能.
研究的目的:
- 为有效的OCT噪音降低开发一个短暂的监督学习框架.
- 为解决OCT成像系统中的域位移问题.
- 以最少的数据来实现脱落模型的高速训练和适应.
主要方法:
- 提出了为海外国家和地区减少噪音的几次监督学习框架.
- 开发了一种方法来制定和解决海外国家和地区的领域转移问题.
- 实施并比较了强度和效率的拟议框架的实际变化.
主要成果:
- 通过只使用一个图像及其基本真相,实现了高速训练 (秒).
- 证明了除斑模型的输出分辨率与源域分辨率有关.
- 展示了改善的样本复杂性,概括性和噪音降低模型的时间效率.
结论:
- 拟议的少量学习框架显著提高了OCT降噪的适应性和效率.
- 该研究提供了对域移动挑战和OCT成像潜在解决方案的见解.
- 该框架显示了超出医学成像的实时计算机视觉应用的潜力.
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