mlsanityData to 7 Oct 2026

Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation

arXiv 2310.11320 ↗First posted 17 Oct 2023eess.IV cs.CVcited by 35

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Abstract (arXiv)

Volume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data. However, the challenges and practical applications extend beyond SSL to settings such as unsupervised domain adaptation (UDA) and semi-supervised domain generalization (SemiDG). This work aims to develop a generic SSL framework that can handle all three settings. We identify two main obstacles to achieving this goal in the existing SSL framework: 1) the weakness of capturing distribution-invariant features; and 2) the tendency for unlabeled data to be overwhelmed by labeled data, leading to over-fitting to the labeled data during training. To address these issues, we propose an Aggregating & Decoupling framework. The aggregating part consists of a Diffusion encoder that constructs a common knowledge set by extracting distribution-invariant features from aggregated information from multiple distributions/domains. The decoupling part consists of three decoders that decouple the training process with labeled and unlabeled data, thus avoiding over-fitting to labeled data, specific domains and classes. We evaluate our proposed framework on four benchmark datasets for SSL, Class-imbalanced SSL, UDA and SemiDG. The results showcase notable improvements compared to state-of-the-art methods across all four settings, indicating the potential of our framework to tackle more challenging SSL scenarios. Code and models are available at: https://github.com/xmed-lab/GenericSSL.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 61 of its 82 references that are in the map. First posted on arXiv 17 Oct 2023.

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61 of 82 references.

References · 61

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38 more
  1. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  2. 2020Semi-Supervised Semantic Segmentation with Cross-Consistency Trainingcited by 375
  3. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
  4. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 229
  5. 2022Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labelscited by 218
  6. 2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 215
  7. 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
  8. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Modelcited by 180
  9. 2021CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learningcited by 174
  10. 2020Disentangling Label Distribution for Long-tailed Visual Recognitioncited by 172
  11. 2020Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Imagescited by 152
  12. 2023DDP: Diffusion Model for Dense Visual Predictioncited by 148
  13. 2019Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentationcited by 138
  14. 2020Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentationcited by 134
  15. 2020Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domainscited by 124
  16. 2020Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularizationcited by 122
  17. 2019Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentationcited by 121
  18. 2023Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentationcited by 115
  19. 2021Semi-supervised Left Atrium Segmentation with Mutual Consistency Trainingcited by 104
  20. 2020Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistencycited by 102
  21. 2022A Generalist Framework for Panoptic Segmentation of Images and Videoscited by 99
  22. 2018Unsupervised domain adaptation for medical imaging segmentation with self-ensemblingcited by 94
  23. 2022Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationcited by 93
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  25. 2020Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptationcited by 81
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Citing papers per year: 2023: 1; 2024: 14; 2025: 13; 2026: 7.

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  1. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  2. 2024AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentationcited by 35
  3. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  4. 2023Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentationcited by 9
  5. 2024Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Imagescited by 9
  6. 2024Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Modelscited by 6
  7. 2024SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imagingcited by 5
  8. 2024RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Modelscited by 4
  9. 2025DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentationcited by 4
  10. 2024SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentationcited by 3
  11. 2025SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentationcited by 3
  12. 2024Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approachcited by 2
23 more
  1. 2025FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentationcited by 2
  2. 2024S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentationcited by 1
  3. 2024Manifold-Aware Local Feature Modeling for Semi-Supervised Medical Image Segmentationcited by 1
  4. 2025Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Taskscited by 1
  5. 2025Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentationcited by 1
  6. 2025Dual-supervised Asymmetric Co-training for Semi-supervised Medical Domain Generalizationcited by 1
  7. 2024Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentationcited by 0
  8. 2024Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregationcited by 0
  9. 2024Dual-Teacher Ensemble Models with Double-Copy-Paste for 3D Semi-Supervised Medical Image Segmentationcited by 0
  10. 2025Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentationcited by 0
  11. 2025Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentationcited by 0
  12. 2025Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentationcited by 0
  13. 2025Mutual Evidential Deep Learning for Medical Image Segmentationcited by 0
  14. 2025Comparison of Segmentation Methods in Remote Sensing for Land Use Land Covercited by 0
  15. 2025Diverse Teaching and Label Propagation for Generic Semi-Supervised Medical Image Segmentationcited by 0
  16. 2025Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDAcited by 0
  17. 2026Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentationcited by 0
  18. 2026SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentationcited by 0
  19. 2026BCMDA: Bidirectional Correlation Maps Domain Adaptation for Mixed Domain Semi-Supervised Medical Image Segmentationcited by 0
  20. 2026Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?cited by 0
  21. 2026SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentationcited by 0
  22. 2026VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentationcited by 0
  23. 2026SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentationcited by 0