mlsanityData to 7 Oct 2026

A Probabilistic U-Net for Segmentation of Ambiguous Images

arXiv 1806.05034 ↗First posted 13 Jun 2018cs.CV cs.LG cs.NEcited by 281

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Cited by 20 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations. We consider the task of learning a distribution over segmentations given an input. To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses. We show on a lung abnormalities segmentation task and on a Cityscapes segmentation task that our model reproduces the possible segmentation variants as well as the frequencies with which they occur, doing so significantly better than published approaches. These models could have a high impact in real-world applications, such as being used as clinical decision-making algorithms accounting for multiple plausible semantic segmentation hypotheses to provide possible diagnoses and recommend further actions to resolve the present ambiguities.

Key facts. Cited by 281 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2018.

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Citing papers per year: 2021: 50; 2022: 49; 2023: 61; 2024: 38; 2025: 39; 2026: 44.

281 papers.

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  1. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  2. 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
  3. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  4. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
  5. 2023Ambiguous Medical Image Segmentation using Diffusion Modelscited by 92
  6. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 78
  7. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  8. 2022MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Predictioncited by 59
  9. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  10. 2021Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Predictioncited by 57
  11. 2022Uncertainty Inspired Underwater Image Enhancementcited by 53
  12. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
38 more
  1. 2021Spatial Uncertainty-Aware Semi-Supervised Crowd Countingcited by 38
  2. 2023Stochastic Segmentation with Conditional Categorical Diffusion Modelscited by 38
  3. 2021Is segmentation uncertainty useful?cited by 37
  4. 2022Transframer: Arbitrary Frame Prediction with Generative Modelscited by 37
  5. 2022Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathologycited by 37
  6. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  7. 2022Learning the Effect of Registration Hyperparameters with HyperMorphcited by 35
  8. 2023Topology-Aware Uncertainty for Image Segmentationcited by 32
  9. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  10. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  11. 2021Variational Autoencoder for Speech Enhancement with a Noise-Aware Encodercited by 28
  12. 2021Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillationcited by 28
  13. 2021QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Resultscited by 28
  14. 2023BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentationcited by 27
  15. 2023A Unified Framework for U-Net Design and Analysiscited by 27
  16. 2023The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detectorcited by 25
  17. 2024ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentationcited by 25
  18. 2024FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transformscited by 25
  19. 2023Calibrating Uncertainty for Semi-Supervised Crowd Countingcited by 22
  20. 2023Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrievalcited by 22
  21. 2021A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimationcited by 21
  22. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  23. 2022Learning Probabilistic Topological Representations Using Discrete Morse Theorycited by 20
  24. 2022Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantificationcited by 20
  25. 2022Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentationcited by 20
  26. 2024Tyche: Stochastic In-Context Learning for Medical Image Segmentationcited by 20
  27. 2021Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentationcited by 19
  28. 2022Efficient Bayesian Uncertainty Estimation for nnU-Netcited by 19
  29. 2021Generative Transformer for Accurate and Reliable Salient Object Detectioncited by 18
  30. 2021Using Soft Labels to Model Uncertainty in Medical Image Segmentationcited by 18
  31. 2022Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentationcited by 18
  32. 2022Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curvecited by 18
  33. 2023Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentationcited by 17
  34. 2021Variational Topic Inference for Chest X-Ray Report Generationcited by 16
  35. 2021Robustness via Uncertainty-aware Cycle Consistencycited by 16
  36. 2022Semantic Segmentation with Active Semi-Supervised Learningcited by 16
  37. 2023Learning to Segment from Noisy Annotations: A Spatial Correction Approachcited by 16
  38. 2021Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentationcited by 15

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