mlsanityData to 7 Oct 2026

CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition

arXiv 2505.14113 ↗First posted 20 May 2025cs.CV cs.LGcited by 2

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Abstract (arXiv)

Most machine learning-based image segmentation models produce pixel-wise confidence scores that represent the model's predicted probability for each class label at every pixel. While this information can be particularly valuable in high-stakes domains such as medical imaging, these scores are heuristic in nature and do not constitute rigorous quantitative uncertainty estimates. Conformal prediction (CP) provides a principled framework for transforming heuristic confidence scores into statistically valid uncertainty estimates. However, applying CP directly to image segmentation ignores the spatial correlations between pixels, a fundamental characteristic of image data. This can result in overly conservative and less interpretable uncertainty estimates. To address this, we propose CONSIGN (Conformal Segmentation Informed by Spatial Groupings via Decomposition), a CP-based method that incorporates spatial correlations to improve uncertainty quantification in image segmentation. Our method generates meaningful prediction sets that come with user-specified, high-probability error guarantees. It is compatible with any pre-trained segmentation model capable of generating multiple sample outputs. We evaluate CONSIGN against two CP baselines across three medical imaging datasets and two COCO dataset subsets, using three different pre-trained segmentation models. Results demonstrate that accounting for spatial structure significantly improves performance across multiple metrics and enhances the quality of uncertainty estimates.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 45 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

32 of 45 references.

References · 32

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20 more
  1. 2022Conformal Risk Controlcited by 290
  2. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  3. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 162
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  6. 2020Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertaintycited by 100
  7. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
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  9. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  10. 2022Predictive Inference with Feature Conformal Predictioncited by 33
  11. 2023Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problemscited by 28
  12. 2024Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertaintycited by 19
  13. 2023Uncertainty Quantification via Neural Posterior Principal Componentscited by 16
  14. 2024Automatically Adaptive Conformal Risk Controlcited by 16
  15. 2023Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithmscited by 14
  16. 2025Conformal Prediction for Image Segmentation Using Morphological Prediction Setscited by 13
  17. 2024Conformal Performance Range Prediction for Segmentation Output Quality Controlcited by 8
  18. 2024Robust Conformal Prediction Using Privileged Informationcited by 7
  19. 2024Conformal confidence sets for biomedical image segmentationcited by 6
  20. 2024Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classificationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Conformal Prediction Sets for Instance Segmentationcited by 1
  2. 2026ConRad: Efficient Conformal Prediction for Radiomicscited by 0