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.
Our research is dedicated to advancing both the theoretical foundations and practical applications of innovative, non-trivial conformal prediction methods aimed at improving the interpretability and reliability of image segmentation. In pre-print [1], we extend pixel-wise CP methods by developing CONSIGN (Conformal Segmentation Informed by Spatial Groupings via Decomposition), a method to leverage spatial correlation for improved results.
The following figure illustrates an example where ignoring the correlation between pixels results in inconsistent predictions and unrealistic pixel combinations. In contrast, our method effectively captures spatial and contextual dependencies, enabling the enforcement of structural constraints. As shown in the figure, our approach achieves a smooth and coherent transition between the segmentation of a sheep and a cow while offering valuable insights into prediction reliability.
