mlsanityData to 7 Oct 2026

Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty

arXiv 2405.05145 ↗First posted 16 Apr 2024cs.CV cs.LGcited by 19

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Abstract (arXiv)

We propose a post-hoc, computationally lightweight method to quantify predictive uncertainty in semantic image segmentation. Our approach uses conformal prediction to generate statistically valid prediction sets that are guaranteed to include the ground-truth segmentation mask at a predefined confidence level. We introduce a novel visualization technique of conformalized predictions based on heatmaps, and provide metrics to assess their empirical validity. We demonstrate the effectiveness of our approach on well-known benchmark datasets and image segmentation prediction models, and conclude with practical insights.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 45 of its 66 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

45 of 66 references.

References · 45

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
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33 more
  1. 2020Classification with Valid and Adaptive Coveragecited by 390
  2. 2022Conformal Risk Controlcited by 290
  3. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 278
  4. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 270
  5. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
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  7. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  8. 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
  9. 2019Addressing Failure Prediction by Learning Model Confidencecited by 183
  10. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  11. 2020Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentationcited by 133
  12. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  13. 2019Nested conformal prediction and quantile out-of-bag ensemble methodscited by 88
  14. 2020Distribution-free binary classification: prediction sets, confidence intervals and calibrationcited by 79
  15. 2019PAC Confidence Sets for Deep Neural Networks via Calibrated Predictioncited by 59
  16. 2023How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Controlcited by 50
  17. 2021Top-label calibration and multiclass-to-binary reductionscited by 46
  18. 2021Is segmentation uncertainty useful?cited by 37
  19. 2022Predictive Inference with Feature Conformal Predictioncited by 33
  20. 2022On Calibrating Semantic Segmentation Models: Analyses and An Algorithmcited by 25
  21. 2020Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Datasetcited by 22
  22. 2020Post Training Uncertainty Calibration of Deep Networks For Medical Image Segmentationcited by 19
  23. 2021Using Soft Labels to Model Uncertainty in Medical Image Segmentationcited by 18
  24. 2021White Paper Machine Learning in Certified Systemscited by 15
  25. 2024Does confidence calibration improve conformal prediction?cited by 12
  26. 2020Quantifying the unknown impact of segmentation uncertainty on image-based simulationscited by 11
  27. 2022CRISP - Reliable Uncertainty Estimation for Medical Image Segmentationcited by 10
  28. 2021Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shiftcited by 8
  29. 2023Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximizationcited by 7
  30. 2018Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networkscited by 5
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  33. 2016Recalling Holistic Information for Semantic Segmentationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 12; 2026: 4.

19 papers.

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  1. 2024Conformal Prediction: A Data Perspectivecited by 25
  2. 2025Conformal Prediction for Image Segmentation Using Morphological Prediction Setscited by 13
  3. 2025Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomographycited by 7
  4. 2024Conformal confidence sets for biomedical image segmentationcited by 6
  5. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  6. 2025WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shiftscited by 5
  7. 2025Efficient Robust Conformal Prediction via Lipschitz-Bounded Networkscited by 4
  8. 2025COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metricscited by 4
  9. 2025Adaptive Conformal Guidance for Learning under Uncertaintycited by 2
  10. 2025Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guaranteescited by 2
  11. 2025CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decompositioncited by 2
  12. 2025Image Super-Resolution with Guarantees via Conformalized Generative Modelscited by 1
7 more
  1. 2026Conformal Prediction Sets for Instance Segmentationcited by 1
  2. 2025Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Predictioncited by 0
  3. 2025Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learningcited by 0
  4. 2025Controlling False Positives in Image Segmentation via Conformal Predictioncited by 0
  5. 2026Class Adaptive Conformal Trainingcited by 0
  6. 2026Anatomically-aware conformal prediction for medical image segmentation with random walkscited by 0
  7. 2026Occupancy-based Quantile Risk Controlcited by 0