Measuring Prediction Uncertainty in Neural Cellular Automata
Neural cellular automata (NCA) provide a lightweight alternative to encoder-decoder segmentation networks. However, it can be difficult to decide when a prediction should be trusted. Here, we study uncertainty estimation for NCA-based medical image segmentation without modifying the underlying architecture or retraining the model. Our approach is motivated by viewing the NCA as a dynamical system where convergent attractors correspond to confident predictions. Concretely, we propose resilience, a simple measure that leverages the intrinsic iterative structure of NCAs by probing the stability of the final prediction under small perturbations of the automaton state. Predictions that return ...
| Authors | Ario Sadafi, Michael Deutges, Nassir Navab et al. |
|---|---|
| Primary category | eess.IV |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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