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Just read 'SegWithU' paper - can perturbation energy uncertainty improve medical segmentation?
Single-forward-pass risk-aware medical image segmentation with uncertainty
The SegWithU paper introduces uncertainty as perturbation energy for medical image segmentation, enabling risk-aware predictions in a single forward pass. This could significantly improve safety in clinical deployments by quantifying model confidence without computational overhead. I'm evaluating implementation with MONAI 1.3.0 and PyTorch 2.3.0 for our radiology pipeline.