Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR
Boundary delineation of anatomical structures in prostate MRI is often compromised by regional ambiguity. Automated segmentation methods relying on global overlap loss functions tend to be insensitive to surface deviations, leading to significant errors in ambiguous regions. Most existing uncertainty-aware methods treat uncertainty as a post-hoc quality indicator or prediction filter. We propose Contrastive Uncertainty Learning (CUL), a supervised framework that uses predictive uncertainty to directly guide representation learning. Using Monte Carlo Dropout, CUL identifies voxel-wise uncertainty and structures the embedding geometry accordingly: low-uncertainty, correctly predicted featur...
| Authors | Bilal Sidiqi, Yipei Wang, Wen Yan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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