Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation.
Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation.
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DOI：
10.1109/tmi.2022.3186698
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发表时间：
2022-11
影响因子：
10.6
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中科院分区：
文献类型：
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域适应技术已被证明可以有效解决医学图像分割中的标签缺陷挑战。然而，传统的基于领域适应的方法通常集中于以与类别无关的方式匹配不同领域之间的全局边缘分布。在本文中，我们提出了一种用于跨模态医学图像分割的双注意域自适应分割网络（DADASeg-Net）。 DADASeg-Net 的关键贡献是一种新颖的双重对抗性注意力机制，它分别从空间和类的角度用两个注意力图来规范域适应模块。具体来说，空间注意力图引导域适应模块关注在适应中难以对齐的区域。类注意力图鼓励领域适应模块捕获特定于类的知识而不是与类无关的知识以进行分布对齐。 DADASeg-Net 在两个具有挑战性的医学图像分割任务中表现出了卓越的性能。
Domain adaptation techniques have been demonstrated to be effective in addressing label deficiency challenges in medical image segmentation. However, conventional domain adaptation based approaches often concentrate on matching global marginal distributions between different domains in a class-agnostic fashion. In this paper, we present a dual-attention domain-adaptative segmentation network (DADASeg-Net) for cross-modality medical image segmentation. The key contribution of DADASeg-Net is a novel dual adversarial attention mechanism, which regularizes the domain adaptation module with two attention maps respectively from the space and class perspectives. Specifically, the spatial attention map guides the domain adaptation module to focus on regions that are challenging to align in adaptation. The class attention map encourages the domain adaptation module to capture class-specific instead of class-agnostic knowledge for distribution alignment. DADASeg-Net shows superior performance in two challenging medical image segmentation tasks.