Semi-supervised Medical Image Segmentation via Query Distribution Consistency
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 21 Nov 2023.
Builds on
26 of 35 references.
References · 26
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2022A ConvNet for the 2020scited by 3,545
- 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
- 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
14 more
- 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
- 2020Semi-Supervised Semantic Segmentation with Cross-Consistency Trainingcited by 375
- 2020MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformerscited by 355
- 2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 215
- 2020Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Imagescited by 152
- 2021Mutual Consistency Learning for Semi-supervised Medical Image Segmentationcited by 107
- 2021Semi-supervised Left Atrium Segmentation with Mutual Consistency Trainingcited by 104
- 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
- 2022Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationcited by 93
- 20183D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Trainingcited by 57
- 2022Semi-supervised Semantic Segmentation with Error Localization Networkcited by 57
- 2020Joint Contrastive Learning with Infinite Possibilitiescited by 31
- 2023Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentationcited by 30
- 2023Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localizationcited by 16
Built on by
Citing papers per year: 2025: 1.
1 paper.