An uncertainty-aware framework with Bayesian ensembling and epistemic uncertainty-augmented training improves lesion segmentation robustness on public multi-tracer PET/CT datasets over standard nnU-Net.
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Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models
An uncertainty-aware framework with Bayesian ensembling and epistemic uncertainty-augmented training improves lesion segmentation robustness on public multi-tracer PET/CT datasets over standard nnU-Net.