mlsanityData to 7 Oct 2026

Deep Co-Training for Semi-Supervised Image Segmentation

arXiv 1903.11233 ↗First posted 27 Mar 2019cs.CVcited by 58

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Abstract (arXiv)

In this paper, we aim to improve the performance of semantic image segmentation in a semi-supervised setting in which training is effectuated with a reduced set of annotated images and additional non-annotated images. We present a method based on an ensemble of deep segmentation models. Each model is trained on a subset of the annotated data, and uses the non-annotated images to exchange information with the other models, similar to co-training. Even if each model learns on the same non-annotated images, diversity is preserved with the use of adversarial samples. Our results show that this ability to simultaneously train models, which exchange knowledge while preserving diversity, leads to state-of-the-art results on two challenging medical image datasets.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 15; 2023: 13; 2024: 7; 2025: 8; 2026: 2.

58 papers.

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  1. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  2. 2021Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentationcited by 61
  3. 2021Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detectioncited by 48
  4. 2022UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentationcited by 45
  5. 2021Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labelscited by 32
  6. 2022SATS: Self-Attention Transfer for Continual Semantic Segmentationcited by 24
  7. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  8. 2021Combating Noise: Semi-supervised Learning by Region Uncertainty Quantificationcited by 18
  9. 2023A Survey on Semi-Supervised Semantic Segmentationcited by 16
  10. 2021Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularizationcited by 14
  11. 2021Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentationcited by 14
  12. 2022Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRIcited by 14
38 more
  1. 2023Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotationscited by 14
  2. 2023Anatomically-aware Uncertainty for Semi-supervised Image Segmentationcited by 13
  3. 2021Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentationcited by 12
  4. 2024Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Surveycited by 12
  5. 2021Belief function-based semi-supervised learning for brain tumor segmentationcited by 11
  6. 2022Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representationscited by 10
  7. 2023Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentationcited by 8
  8. 2024Generative AI Enables Medical Image Segmentation in Ultra Low-Data Regimescited by 8
  9. 2025Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learningcited by 8
  10. 2021Segmentation with mixed supervision: Confidence maximization helps knowledge distillationcited by 6
  11. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  12. 2025MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentationcited by 6
  13. 2022Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentationcited by 5
  14. 2022Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweightingcited by 5
  15. 2022Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directionscited by 5
  16. 2023Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classificationcited by 5
  17. 2025Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentationcited by 5
  18. 2022Application of belief functions to medical image segmentation: A reviewcited by 4
  19. 2023Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentationcited by 4
  20. 2023Semi-Supervised Panoptic Narrative Groundingcited by 4
  21. 2024EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentationcited by 4
  22. 2022Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentationcited by 3
  23. 2022Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentationcited by 3
  24. 2023Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentationcited by 3
  25. 2023Semi-supervised Learning for Segmentation of Bleeding Regions in Video Capsule Endoscopycited by 3
  26. 2024PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentationcited by 3
  27. 2024DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Differencecited by 3
  28. 2025Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustnesscited by 3
  29. 2021Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidancecited by 2
  30. 2022On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentationcited by 2
  31. 2022PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Trainingcited by 2
  32. 2022ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particlescited by 1
  33. 2023Self-training with dual uncertainty for semi-supervised medical image segmentationcited by 1
  34. 2023Semi-Supervised Medical Image Segmentation with Co-Distribution Alignmentcited by 1
  35. 2023Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentationcited by 1
  36. 2021Context-aware virtual adversarial training for anatomically-plausible segmentationcited by 0
  37. 2021Diversified Multi-prototype Representation for Semi-supervised Segmentationcited by 0
  38. 2022L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmentercited by 0

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