mlsanityData to 7 Oct 2026

Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation

arXiv 2110.08762 ↗First posted 17 Oct 2021cs.CVcited by 61

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Abstract (arXiv)

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). Builds on 41 of its 66 references that are in the map. First posted on arXiv 17 Oct 2021.

Builds on

41 of 66 references.

References · 41

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  5. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  6. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  7. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  8. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
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  10. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
  11. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  12. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
29 more
  1. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 583
  2. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  3. 2020Semi-Supervised Semantic Segmentation with Cross-Consistency Trainingcited by 375
  4. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  5. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 278
  6. 2019CE-Net: Context Encoder Network for 2D Medical Image Segmentationcited by 219
  7. 2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 215
  8. 2015DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentationcited by 158
  9. 2020Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Imagescited by 152
  10. 2019Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentationcited by 138
  11. 2019Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networkscited by 116
  12. 2016Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriorscited by 82
  13. 2020Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptationcited by 81
  14. 2019Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentationcited by 75
  15. 2019Deep Co-Training for Semi-Supervised Image Segmentationcited by 58
  16. 2019Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentationcited by 55
  17. 2018Universal Semi-Supervised Semantic Segmentationcited by 50
  18. 2020Double-Uncertainty Weighted Method for Semi-supervised Learningcited by 44
  19. 20183D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizescited by 42
  20. 2020Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentationcited by 30
  21. 2017Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networkscited by 28
  22. 2019Curriculum semi-supervised segmentationcited by 27
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  24. 2018Recurrent neural networks for aortic image sequence segmentation with sparse annotationscited by 25
  25. 2019Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Netcited by 15
  26. 2018Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Imagescited by 14
  27. 2019Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentationcited by 14
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  29. 2020A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervisioncited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 18; 2024: 14; 2025: 14; 2026: 7.

61 papers.

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  1. 2023Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentationcited by 115
  2. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  3. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  4. 2022NP-Match: When Neural Processes meet Semi-Supervised Learningcited by 25
  5. 2022MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularizationcited by 22
  6. 2022MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recoverycited by 22
  7. 2022Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentationcited by 21
  8. 2023UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentationcited by 21
  9. 2022PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillationcited by 16
  10. 2023Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentationcited by 15
  11. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 14
  12. 2024Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentationcited by 14
38 more
  1. 2023Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistencycited by 11
  2. 2024Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translationcited by 9
  3. 2024SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentationcited by 9
  4. 20233D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networkscited by 7
  5. 2023U-Netmer: U-Net meets Transformer for medical image segmentationcited by 6
  6. 2023Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detectioncited by 6
  7. 2025MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentationcited by 6
  8. 20233D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotationscited by 5
  9. 2023NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentationcited by 5
  10. 2024An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentationcited by 5
  11. 2024The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentationcited by 5
  12. 2025Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentationcited by 5
  13. 2024EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentationcited by 4
  14. 2024Deep Learning for Pancreas Segmentation: a Systematic Reviewcited by 4
  15. 2022Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentationcited by 3
  16. 2023Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanismcited by 3
  17. 2025Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentationcited by 3
  18. 2025Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialistscited by 3
  19. 2025Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Frameworkcited by 3
  20. 2023Probabilistic Domain Adaptation for Biomedical Image Segmentationcited by 2
  21. 2024Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentationcited by 2
  22. 2026Bidirectional Channel-selective Semantic Interaction for Semi-Supervised Medical Segmentationcited by 2
  23. 2021MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labelscited by 1
  24. 2023NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learningcited by 1
  25. 2023Self-training with dual uncertainty for semi-supervised medical image segmentationcited by 1
  26. 2023Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentationcited by 1
  27. 2023Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentationcited by 1
  28. 2024Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentationcited by 1
  29. 2024Manifold-Aware Local Feature Modeling for Semi-Supervised Medical Image Segmentationcited by 1
  30. 2026Domain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignmentcited by 1
  31. 2026Fully Differentiable Bidirectional Dual-Task Synergistic Learning for Semi-Supervised 3D Medical Image Segmentationcited by 1
  32. 2026Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentationcited by 1
  33. 2023Mixed-order self-paced curriculum learning for universal lesion detectioncited by 0
  34. 2024Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learningcited by 0
  35. 2024AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentationcited by 0
  36. 2024Dual-Teacher Ensemble Models with Double-Copy-Paste for 3D Semi-Supervised Medical Image Segmentationcited by 0
  37. 2024DiM: f-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentationcited by 0
  38. 2025Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentationcited by 0

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