Boundary-weighted logit consistency improves calibration of segmentation networks

• URL: http://arxiv.org/abs/2307.08163v1
• Date: Sun, 16 Jul 2023 22:13:28 GMT
• Title: Boundary-weighted logit consistency improves calibration of segmentation networks
• Authors: Neerav Karani, Neel Dey, Polina Golland
• Abstract summary: We show that logit consistency 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.
• Score: 6.980357450216633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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