# Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation

arXiv: https://arxiv.org/abs/2112.00735 · HTML: https://mlsanity.com/paper/2112.00735/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 44 references that are in the map.
- First posted on arXiv 1 Dec 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Meta Pseudo Labels](https://mlsanity.com/paper/2003.10580/) (2020)
- [ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring](https://mlsanity.com/paper/1911.09785/) (2019)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning](https://mlsanity.com/paper/2101.06329/) (2021)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [PseudoSeg: Designing Pseudo Labels for Semantic Segmentation](https://mlsanity.com/paper/2010.09713/) (2020)
- [The Contextual Loss for Image Transformation with Non-Aligned Data](https://mlsanity.com/paper/1803.02077/) (2018)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation](https://mlsanity.com/paper/2104.13243/) (2021)
- [Deep Metric Transfer for Label Propagation with Limited Annotated Data](https://mlsanity.com/paper/1812.08781/) (2018)
- [Semi-Supervised Learning with Scarce Annotations](https://mlsanity.com/paper/1905.08845/) (2019)
- [Semi-supervised semantic segmentation needs strong, varied perturbations](https://mlsanity.com/paper/1906.01916/) (2019)

## Built on by (most cited first, 30 of 33)

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [Directional Connectivity-based Segmentation of Medical Images](https://mlsanity.com/paper/2304.00145/) (2023)
- [CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection](https://mlsanity.com/paper/2212.00244/) (2022)
- [Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding](https://mlsanity.com/paper/2210.03416/) (2022)
- [Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling](https://mlsanity.com/paper/2306.03934/) (2023)
- [Referring Atomic Video Action Recognition](https://mlsanity.com/paper/2407.01872/) (2024)
- [SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation](https://mlsanity.com/paper/2308.13759/) (2023)
- [Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistency](https://mlsanity.com/paper/2311.16447/) (2023)
- [COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings](https://mlsanity.com/paper/2206.15073/) (2022)
- [Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2508.09014/) (2025)
- [Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2306.14293/) (2023)
- [Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction](https://mlsanity.com/paper/2309.04956/) (2023)
- [ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model](https://mlsanity.com/paper/2406.10855/) (2024)
- [DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising](https://mlsanity.com/paper/2412.08806/) (2024)
- [MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation](https://mlsanity.com/paper/2510.01532/) (2025)
- [Bidirectional Channel-selective Semantic Interaction for Semi-Supervised Medical Segmentation](https://mlsanity.com/paper/2601.05855/) (2026)
- [Exemplar Learning for Medical Image Segmentation](https://mlsanity.com/paper/2204.01713/) (2022)
- [Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring](https://mlsanity.com/paper/2305.09368/) (2023)
- [Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2412.15380/) (2024)
- [CAST: Cluster-Aware Self-Training for Tabular Data via Reliable Confidence](https://mlsanity.com/paper/2310.06380/) (2023)
- [Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation](https://mlsanity.com/paper/2409.03228/) (2024)
- [Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation](https://mlsanity.com/paper/2410.10366/) (2024)
- [Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation](https://mlsanity.com/paper/2404.11812/) (2024)
- [GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.09355/) (2025)
- [Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.09408/) (2025)
- [Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation](https://mlsanity.com/paper/2503.17896/) (2025)
- [P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.15861/) (2025)
- [CoATA: Effective Co-Augmentation of Topology and Attribute for Graph Neural Networks](https://mlsanity.com/paper/2506.22299/) (2025)
- [Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications](https://mlsanity.com/paper/2510.03318/) (2025)

## Abstract (arXiv)

Producing densely annotated data is a difficult and tedious task for medical imaging applications. To address this problem, we propose a novel approach to generate supervision for semi-supervised semantic segmentation. We argue that visually similar regions between labeled and unlabeled images likely contain the same semantics and therefore should share their label. Following this thought, we use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantics of the best fitting pixel in a reference set. This way, we avoid pitfalls such as confirmation bias, common in purely prediction-based pseudo-labeling. Since our method does not require any architectural changes or accompanying networks, one can easily insert it into existing frameworks. We achieve the same performance as a standard fully supervised model on X-ray anatomy segmentation, albeit 95% fewer labeled images. Aside from an in-depth analysis of different aspects of our proposed method, we further demonstrate the effectiveness of our reference-guided learning paradigm by comparing our approach against existing methods for retinal fluid segmentation with competitive performance as we improve upon recent work by up to 15% mean IoU.