Semi-supervised learning is increasingly popular in medical image
segmentation due to its ability to leverage large amounts of unlabeled data to
extract additional information. However, most existing semi-supervised
segmentation methods focus only on extracting information from unlabeled data.
In this paper, we propose a novel Dual KMax UX-Net framework that leverages
labeled data to guide the extraction of information from unlabeled data. Our
approach is based on a mutual learning strategy that incorporates two modules:
3D UX-Net as our backbone meta-architecture and KMax decoder to enhance the
segmentation performance. Extensive experiments on the Atrial Segmentation
Challenge dataset have shown that our method can significantly improve
performance by merging unlabeled data. Meanwhile, our framework outperforms
state-of-the-art semi-supervised learning methods on 10\% and 20\% labeled
settings. Code located at: this https URL

我们提出了一种基于 Dual KMax UX-Net 的新型半监督学习框架，该框架利用标记数据指导对未标记数据的信息提取，并通过合并未标记数据显著提高性能。实验证明，我们的方法在 Atrial Segmentation Challenge 数据集上优于现有的半监督学习方法，尤其是在 10% 和 20% 标记设置下。