Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions for …