Semi-supervised Medical Image Segmentation via Query Distribution Consistency
This work addresses the problem of limited labeled data in medical image segmentation for healthcare applications, representing an incremental improvement over existing methods.
The paper tackles semi-supervised medical image segmentation by proposing a Dual KMax UX-Net framework that uses labeled data to guide information extraction from unlabeled data, achieving state-of-the-art performance on the Atrial Segmentation Challenge dataset with 10% and 20% labeled settings.
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.