A Smart Calibrated Probabilistic U-Net for Aleatoric and Epistemic Uncertainty Quantification In Medical Image Segmentation
Image segmentation is a critical yet challenging step in the radiotherapy workflow. Although many AI-based segmentation models report high accuracy, substantial epistemic and aleatoric uncertainties persist. This work proposes a probabilistic U-Net framework...
Poster Program · Therapy Physics