Semi-supervised Medical Image Segmentation via Query Distribution
  Consistency
Rong Wu Dehua Li Cong Zhang
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: https://github.com/Rows21/DK-UXNet.