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Neural network prediction probabilities and accuracy are often onlyweakly-correlated. Inherent label ambiguity in training data for imagesegmentation aggravates such miscalibration. We show that logit consistencyacross stochastic transformations acts as a spatially varying regularizer thatprevents overconfident predictions at pixels with ambiguous labels. Ourboundary-weighted extension of this regularizer provides state-of-the-artcalibration for prostate and heart MRI segmentation.

Boundary-weighted logit consistency improves calibration of segmentation networks