# Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation

arXiv: https://arxiv.org/abs/2301.04465 · HTML: https://mlsanity.com/paper/2301.04465/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 34 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 27 of 27)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2003.03773/) (2020)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [Adversarial Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1802.07934/) (2018)
- [Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images](https://mlsanity.com/paper/2007.10732/) (2020)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [Guided Collaborative Training for Pixel-wise Semi-Supervised Learning](https://mlsanity.com/paper/2008.05258/) (2020)
- [Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2111.12903/) (2021)
- [Mutual Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.09960/) (2021)
- [Semi-supervised Left Atrium Segmentation with Mutual Consistency Training](https://mlsanity.com/paper/2103.02911/) (2021)
- [Double-Uncertainty Weighted Method for Semi-supervised Learning](https://mlsanity.com/paper/2010.09298/) (2020)
- [GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference](https://mlsanity.com/paper/2112.14015/) (2021)

## Built on by (most cited first, 25 of 25)

- [Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2412.13742/) (2024)
- [PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2409.05122/) (2024)
- [Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer](https://mlsanity.com/paper/2407.08460/) (2024)
- [Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning](https://mlsanity.com/paper/2406.17902/) (2024)
- [Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2412.15380/) (2024)
- [UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation](https://mlsanity.com/paper/2604.09169/) (2026)
- [Adaptive Mix for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.21586/) (2024)
- [Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation](https://mlsanity.com/paper/2510.14244/) (2025)
- [Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2607.04249/) (2026)
- [Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective](https://mlsanity.com/paper/2405.09777/) (2024)
- [Collaborative Static-Dynamic Teaching: A Semi-Supervised Framework for Stripe-Like Space Target Detection](https://mlsanity.com/paper/2408.05029/) (2024)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)
- [Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation](https://mlsanity.com/paper/2504.11856/) (2025)
- [Decoupled Competitive Framework for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.24667/) (2025)
- [C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2506.07368/) (2025)
- [Semi-Supervised Dual-Threshold Contrastive Learning for Ultrasound Image Classification and Segmentation](https://mlsanity.com/paper/2508.02265/) (2025)
- [Instance-Aware Robust Consistency Regularization for Semi-Supervised Nuclei Instance Segmentation](https://mlsanity.com/paper/2510.09329/) (2025)
- [Semi-Supervised Masked Autoencoders: Unlocking Vision Transformer Potential with Limited Data](https://mlsanity.com/paper/2601.20072/) (2026)
- [SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detection](https://mlsanity.com/paper/2602.23447/) (2026)
- [Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2603.05202/) (2026)
- [CPS4: Class Prompt driven Semi-Supervised Spine Segmentation with Class-specific Consistency Constraint](https://mlsanity.com/paper/2606.15802/) (2026)
- [Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2606.17958/) (2026)
- [Decoupled Single-Mask Annotation Noise Detection via Cross-Sectional Patch Self-Consistency](https://mlsanity.com/paper/2607.05965/) (2026)
- [CoDiR: Confidence-Guided Diffusion Refinement for Semi-Supervised Histopathology Segmentation](https://mlsanity.com/paper/2608.11807/) (2026)
- [CoGameReg: Cooperative-Game-Based Collaborative Semi-Supervised Regression for Seismic Reservoir Property Prediction](https://mlsanity.com/paper/2610.12884/) (2026)

## Abstract (arXiv)

Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inputs during training. To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. Concretely, UCMT consists of two main components: 1) collaborative mean-teacher (CMT) for encouraging model disagreement and performing co-training between the sub-networks, and 2) uncertainty-guided region mix (UMIX) for manipulating the input images according to the uncertainty maps of CMT and facilitating CMT to produce high-confidence pseudo labels. Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation. Extensive experiments on four public medical image datasets including 2D and 3D modalities demonstrate the superiority of UCMT over the state-of-the-art. Code is available at: https://github.com/Senyh/UCMT.