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EDRL:在语义分割中用于无监督域适应的输入导向解的表示学习

Runze Wang¹, Qin Zhou¹, Guoyan Zheng¹

¹Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai, 200240, China.

Computer methods and programs in biomedicine

|August 2, 2023

概括

本研究介绍了以透为导向的解表示学习 (EDRL) 用于在医疗图像细分中无监督的域适应,提高了在没有目标注释的情况下对域转移的稳定性.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
机器学习机器学习

背景情况:

深度学习模型在医疗成像中的领域转移中扎.
无监督域调整 (UDA) 旨在在没有目标域注释的情况下弥合这一差距.

研究的目的:

为医疗图像开发一个强大的跨领域细分方法.
为了应对深度学习模型中领域转移的挑战.

主要方法:

拟用于UDA的引式解表示学习 (EDRL).
集成的图像和特征对齐使用脱的表示和对抗性学习.
通过软门实现了动态特征选择机制.

主要成果:

在CT-MR和多序心脏MR数据集上取得了最先进的结果.
在跨领域细分任务中表现出高性能 (例如,CT-MR上84.8%的DSC).

结论:

EDRL模型有效地增强了跨领域的医疗图像细分.
实验结果验证了拟议方法在处理域转移方面的有效性.

关键词:

不纠的表示学习学习.基于的对抗式学习.医疗图像细分医疗图像细分无监督的域名适应

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