# RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

arXiv: https://arxiv.org/abs/2301.05500 · HTML: https://mlsanity.com/paper/2301.05500/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [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)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [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)
- [PseudoSeg: Designing Pseudo Labels for Semantic Segmentation](https://mlsanity.com/paper/2010.09713/) (2020)
- [Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images](https://mlsanity.com/paper/2007.10732/) (2020)
- [Semi-supervised Semantic Segmentation with Directional Context-aware Consistency](https://mlsanity.com/paper/2106.14133/) (2021)
- [Mutual Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.09960/) (2021)
- [Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency](https://mlsanity.com/paper/2012.07042/) (2020)
- [Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2108.09025/) (2021)
- [Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer](https://mlsanity.com/paper/2112.04894/) (2021)
- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model](https://mlsanity.com/paper/1903.01248/) (2019)
- [Prior Attention Network for Multi-Lesion Segmentation in Medical Images](https://mlsanity.com/paper/2110.04735/) (2021)

## Built on by (most cited first, 27 of 27)

- [ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models](https://mlsanity.com/paper/2302.07257/) (2023)
- [Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2405.00378/) (2024)
- [SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization](https://mlsanity.com/paper/2312.06316/) (2023)
- [Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2312.16039/) (2023)
- [When Confidence Fails: Revisiting Pseudo-Label Selection in Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2509.16704/) (2025)
- [Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2306.14293/) (2023)
- [CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation](https://mlsanity.com/paper/2405.00354/) (2024)
- [Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation](https://mlsanity.com/paper/2403.12767/) (2024)
- [FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation](https://mlsanity.com/paper/2306.15189/) (2023)
- [Semi-Supervised Medical Image Segmentation via Dual Networks](https://mlsanity.com/paper/2505.17690/) (2025)
- [Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2308.16573/) (2023)
- [Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.05248/) (2024)
- [Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy](https://mlsanity.com/paper/2410.07460/) (2024)
- [ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification](https://mlsanity.com/paper/2411.05901/) (2024)
- [AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach](https://mlsanity.com/paper/2501.15363/) (2025)
- [Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation](https://mlsanity.com/paper/2502.11456/) (2025)
- [Knowledge Consultation for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2503.10693/) (2025)
- [UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets](https://mlsanity.com/paper/2311.10251/) (2023)
- [Learning Semi-Supervised Medical Image Segmentation from Spatial Registration](https://mlsanity.com/paper/2409.10422/) (2024)
- [Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.09408/) (2025)
- [Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning](https://mlsanity.com/paper/2503.14013/) (2025)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (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)
- [Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation](https://mlsanity.com/paper/2508.06517/) (2025)
- [Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling](https://mlsanity.com/paper/2509.13084/) (2025)
- [Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation](https://mlsanity.com/paper/2601.07377/) (2026)

## Abstract (arXiv)

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss to the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS.