论文标题
无监督的域适应心脏分割：朝向结构相互信息最大化
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
论文作者
论文摘要
无监督的域适应方法最近在各种医学图像分割任务中成功了。报告的作品通常通过对齐域不变特征并最大程度地减少特定于域的差异来解决域移位问题。当特定域之间的差异和不同域之间的差异很小时，该策略效果很好。但是，这些模型对各种成像方式的概括能力仍然是一个重大挑战。本文介绍了UDA-VAE ++，这是一种无监督的域适应框架，用于心脏分割，并具有紧凑的损失函数下限。为了估算这个新的下限，我们使用全局估计器，局部估计器和先前的信息匹配估计器开发了新的结构共同信息估计（SMIE）块，以最大程度地提高重建和分割任务之间的相互信息。具体而言，我们设计了一种新型的顺序重新聚集方案，该方案可以从低分辨率潜在空间到高分辨率潜在空间进行信息流和方差校正。基准心脏分割数据集的全面实验表明，我们的模型在定性和定量上都优于先前的最先进。该代码可从https://github.com/louey233/toward-mutual-information} {https://github.com/louey233/toward-mutual-information获得
Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively. The code is available at https://github.com/LOUEY233/Toward-Mutual-Information}{https://github.com/LOUEY233/Toward-Mutual-Information
