论文标题
提高半监督域将来医学图像分割的伪标签质量
Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation
论文作者
论文摘要
将医学图像分割算法推广到看不见的领域是计算机辅助诊断和手术的重要研究主题。大多数现有方法都需要每个源域中的完全标记的数据集。尽管一些研究人员开发了一种半监督的域广义方法，但它仍然需要域标签。本文提出了一种新型的置信度跨伪监督算法，用于半监督域广义医学图像分割。主要目标是增强未知分布中未标记图像的伪标签质量。为了实现这一目标，我们执行傅立叶变换，以学习跨域的低级统计信息，并增加图像以结合跨域信息。借助这些增强功能，我们将输入输入到感知到信心的交叉伪监督网络中，以衡量伪标签的差异，并将网络正规化以使用更自信的伪标签学习。我们的方法在公共数据集（即M＆M和SCGM）上设置了新记录。值得注意的是，如果没有使用域标签，我们的方法就超过了先前的ART，甚至使用2％标记的数据，在M＆MS数据集上使用域标签的骰子上的骰子却增加了11.67％。代码可在https://github.com/xmed-lab/epl_semidg上找到。
Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery. Most existing methods require a fully labeled dataset in each source domain. Although some researchers developed a semi-supervised domain generalized method, it still requires the domain labels. This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. The main goal is to enhance the pseudo label quality for unlabeled images from unknown distributions. To achieve it, we perform the Fourier transformation to learn low-level statistic information across domains and augment the images to incorporate cross-domain information. With these augmentations as perturbations, we feed the input to a confidence-aware cross pseudo supervision network to measure the variance of pseudo labels and regularize the network to learn with more confident pseudo labels. Our method sets new records on public datasets, i.e., M&Ms and SCGM. Notably, without using domain labels, our method surpasses the prior art that even uses domain labels by 11.67% on Dice on M&Ms dataset with 2% labeled data. Code is available at https://github.com/XMed-Lab/EPL_SemiDG.
