GMM-PA:高斯混合物以模型为基础的原型对齐用于多源域适应在多片分割中的多源域适应
Yue Wang1, Hongqing Zhu2, Ziying Wang1
1School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.
Journal of imaging informatics in medicine
|November 5, 2025
概括
这项研究引入了在结肠镜图像中聚细分的新方法,提高了不同数据源的准确性. 高斯混合模型引导的原型对齐 (GMM-PA) 网络通过克服域转移挑战来增强结直肠癌检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 在结肠镜检查中精确的息肉细分对于早期发现结肠直肠癌至关重要.
- 深度学习模型在跨中心结肠镜数据中面临性能问题,原因是域移动.
研究的目的:
- 开发一个强大的多源域适应网络,用于跨域聚细分.
- 在不同中心的未见数据上增强聚细分模型的性能.
主要方法:
- 提出了一个高斯混合模型引导的原型对齐 (GMM-PA) 网络.
- 实现了一个图像预处理模块,用于域差异缓解 (LAB颜色转换,里埃转换光谱交换).
- 利用混合CNN-Mamba架构进行特征提取,并为域不变特征采用对抗式学习.
主要成果:
- 该GMM-PA方法在跨域聚细分方面表现出卓越的性能.
- 在三个公共数据集中获得了0.8396的平均子得分和0.8449的mIoU.
- 在跨领域的设置中超越现有的最先进的方法.
结论:
- 拟议的GMM-PA网络有效地解决了结肠镜聚细分中的域转移问题.
- 该方法提供了一个强大的解决方案,通过增强的图像分析来改善结直肠癌的检测.
- 混合CNN-Mamba架构和GMM引导的对齐有助于优越的跨域概括.
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