Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty
Open accessAbstract
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.
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Other conformal approaches reduce false negatives by lowering score thresholds or by morphological dilation of the predicted mask.
“Other conformal approaches reduce false negatives by lowering score thresholds [2, 13, 5] or by morphological dilation of the predicted mask [14].”
From Controlling False Positives in Image Segmentation via Conformal Prediction (2025) · cites this paper as [13]
The [14] citation is attached to morphological dilation, not to lowering score thresholds. Section 4.1.1 describes constructing multi-labeled masks by thresholding pixel-wise softmax scores, but the passages found do not address morphological dilation of a predicted mask. The cited copy therefore does not verify the claim attached to [14]. (LLM, conf=0.72)
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- Authors
- Mossina, L., Dalmau, J., & Andéol, L.
- Affiliations
- IRT Saint Exupéry (FR)
- Université Toulouse III - Paul Sabatier (FR)
- ORCID
- Andéol, Léo
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2024-06-17
- Pages
- 3574-3584
- OpenAlex
- W4396817150
- Cited by
- 18
- References
- 56
- Citations by year
- 2026: 7 · 2025: 11
- FWCI
- 3.72
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- conformal prediction, semantic segmentation, forecast uncertainty, prediction sets, heatmaps, confidence intervals
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