计算机科学
图像分割
适应(眼睛)
计算机视觉
人工智能
傅里叶域
图像(数学)
域适应
分割
领域(数学分析)
尺度空间分割
傅里叶变换
数学
光学
物理
分类器(UML)
数学分析
作者
Kuan-Fu Liu,Yie‐Tarng Chen,Wen‐Hsien Fang,Boyang Chen
标识
DOI:10.1109/icasi60819.2024.10547859
摘要
Domain adaptation plays a critical role in medical image segmentation, addressing variations stemming from diverse machine types and specifications. In this paper, we propose a new Fourier Style Transfer (FST)-based approach tailored specifically for this purpose. Our method begins by employing similarity measurement to select an appropriate target image, thereby mitigating domain gaps. Subsequently, we integrate a segmentation label matching strategy to emphasize significant regions within the target image, effectively diminishing the influence of noisy features. Leveraging a source-free domain adaptation architecture that incorporates entropy minimization and class-ratio priority, our framework demonstrates robust performance in challenging medical segmentation tasks. Simulation outcomes underscore the efficacy of our approach over existing methods, as evidenced by superior results achieved on two widely used medical datasets.
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