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. Code is available at https://github.com/neerakara/BWCR.
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Acknowledgements
This research is supported by NIH NIBIB NAC P41EB015902, IBM, and the Swiss National Science Foundation under project P500PT-206955.
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Karani, N., Dey, N., Golland, P. (2023). Boundary-Weighted Logit Consistency Improves Calibration of Segmentation Networks. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_36
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DOI: https://doi.org/10.1007/978-3-031-43898-1_36
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