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arXiv · 2307.08163

Boundary-weighted logit consistency improves calibration of segmentation networks

Neerav Karani·Neel Dey·Polina Golland
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Abstract

Neural network prediction probabilities and accuracy are often only weakly-correlated. Inherent label ambiguity in training data for image segmentation aggravates such miscalibration. We show that logit consistency across stochastic transformations acts as a spatially varying regularizer that prevents overconfident predictions at pixels with ambiguous labels. Our boundary-weighted extension of this regularizer provides state-of-the-art calibration for prostate and heart MRI segmentation.

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Neerav Karani, Neel Dey, Polina Golland. 2023-07-16. Boundary-weighted logit consistency improves calibration of segmentation networks. https://arxiv.org/abs/2307.08163

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