幻觉域概括网络,具有域意识的动态表示,用于医学图像细分
Minjun Wang1, Houjin Chen1, Yanfeng Li1
1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
概括
这项研究引入了一种用于医疗图像细分的新型网络,该网络可以改善不同领域的概括性. 该方法使用动态幻觉和表示来适应模型的新数据,提高在看不见的医疗图像的性能.
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:
- 医疗图像细分模型在不同的采集协议和领域中与性能下降作斗争.
- 这种退化往往是源数据过度适应和适应新目标领域的适应性不佳造成的.
研究的目的:
- 开发一种用于医疗图像细分的新型网络,增强跨未见域的概括性.
- 解决过度拟合问题,提高细分模型中的动态适应性.
主要方法:
- 提出了一个幻觉域泛化网络,具有域意识的动态表示.
- 引入了一个不确定性意识的动态幻觉模块,使用贝齐尔曲线和不确定性意识的偏移来生成合成图像.
- 开发了一个域意识的动态表示模块,用于将输入特征映射到统一的源域空间,使用样式原型和相似度权重.
主要成果:
- 提出的方法有效地打破了源域限制,同时保留了解剖结构.
- 它减轻了模型过度适应特定的源域样式.
- 对 fundus 和前列腺MRI数据集的实验显示,与最先进的方法相比,其性能优越.
结论:
- 新的"训练期间的幻觉,测试期间的动态表示"方案显著改善了医学图像细分的概括性.
- 这种方法有效地减轻了由域转移引起的性能下降.
- 这种方法为跨领域医疗图像细分任务提供了强大的解决方案.
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