mlsanityData to 7 Oct 2026

PHiSeg: Capturing Uncertainty in Medical Image Segmentation

arXiv 1906.04045 ↗First posted 7 Jun 2019eess.IV cs.LG stat.MLcited by 128

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Abstract (arXiv)

Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different styles of annotating. The majority of current state-of-the-art methods do not account for such ambiguities but rather learn a single mapping from image to segmentation. In this work, we propose a novel method to model the conditional probability distribution of the segmentations given an input image. We derive a hierarchical probabilistic model, in which separate latent variables are responsible for modelling the segmentation at different resolutions. Inference in this model can be efficiently performed using the variational autoencoder framework. We show that our proposed method can be used to generate significantly more realistic and diverse segmentation samples compared to recent related work, both, when trained with annotations from a single or multiple annotators.

Key facts. Cited by 128 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2019.

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Citing papers per year: 2021: 21; 2022: 21; 2023: 26; 2024: 17; 2025: 25; 2026: 18.

128 papers.

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  1. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  2. 2023Ambiguous Medical Image Segmentation using Diffusion Modelscited by 92
  3. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  4. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  5. 2021Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Predictioncited by 57
  6. 2022Uncertainty Inspired Underwater Image Enhancementcited by 53
  7. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  8. 2021Spatial Uncertainty-Aware Semi-Supervised Crowd Countingcited by 38
  9. 2023Stochastic Segmentation with Conditional Categorical Diffusion Modelscited by 38
  10. 2023Topology-Aware Uncertainty for Image Segmentationcited by 32
  11. 2021Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotationscited by 31
  12. 2021QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Resultscited by 28
38 more
  1. 2023BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentationcited by 27
  2. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agentscited by 27
  3. 2023The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detectorcited by 25
  4. 2024ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentationcited by 25
  5. 2024FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transformscited by 25
  6. 2023Calibrating Uncertainty for Semi-Supervised Crowd Countingcited by 22
  7. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  8. 2024Tyche: Stochastic In-Context Learning for Medical Image Segmentationcited by 20
  9. 2022Efficient Bayesian Uncertainty Estimation for nnU-Netcited by 19
  10. 2021Generative Transformer for Accurate and Reliable Salient Object Detectioncited by 18
  11. 2021Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Datacited by 18
  12. 2021Using Soft Labels to Model Uncertainty in Medical Image Segmentationcited by 18
  13. 2021Challenges for machine learning in clinical translation of big data imaging studiescited by 15
  14. 2021Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flowscited by 13
  15. 2022Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Predictioncited by 13
  16. 2022Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Expertscited by 13
  17. 2023That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentationcited by 13
  18. 2023Anatomically-aware Uncertainty for Semi-supervised Image Segmentationcited by 13
  19. 2021Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentationcited by 12
  20. 2024QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challengecited by 12
  21. 2021Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentationcited by 11
  22. 2021On the relationship between calibrated predictors and unbiased volume estimationcited by 11
  23. 2022Generalized Probabilistic U-Net for medical image segementationcited by 10
  24. 2023Uncertainty Estimation and Propagation in Accelerated MRI Reconstructioncited by 10
  25. 2023Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantificationcited by 9
  26. 2024Diversified and Personalized Multi-rater Medical Image Segmentationcited by 9
  27. 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysiscited by 8
  28. 2022Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentificationcited by 8
  29. 2024Conformal Performance Range Prediction for Segmentation Output Quality Controlcited by 8
  30. 2022Uncertainty-Driven Action Quality Assessmentcited by 7
  31. 2023Effect of latent space distribution on the segmentation of images with multiple annotationscited by 7
  32. 2023Building Extraction from Remote Sensing Images via an Uncertainty-Aware Networkcited by 7
  33. 2022Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertaintycited by 6
  34. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  35. 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
  36. 2022Improved post-hoc probability calibration for out-of-domain MRI segmentationcited by 5
  37. 2024MedUHIP: Towards Human-In-the-Loop Medical Segmentationcited by 5
  38. 2025Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matchingcited by 5

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