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专注的持续生成的自我训练无监督的领域适应医疗图像翻译
Xiaofeng Liu1, Jerry L Prince2, Fangxu Xing1
1Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114.
ArXiv
|June 9, 2023
概括
本研究介绍了生成自我训练 (GST),这是一种用于图像翻译任务的新型无监督域适应方法. 通过量化不确定性和专注于可靠数据,GST有效地解决了域名转移问题,优于现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 无监督域调整 (UDA) 对于将模型应用于新数据域至关重要.
- 自我训练方法在区分任务中表现出色,但在像图像翻译这样的生成任务中未被充分探索.
- 域移动在医学成像和其他领域构成了重大挑战.
结论:
- GST框架在交叉扫描器/中心场景中显著提高了图像翻译性能.
- 在 MR 图像翻译等任务上优于对抗训练 UDA 方法.
- 证明了自我训练对复杂的生成域适应问题的潜力.
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