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Abstract #4301

Applying Venn-Abers Predictors to Calibrate White Matter Hyperintensity Segmentations from Brain Images

Karl Landheer1, Karl Landheer1, Benjamin Geraghty1, Joseph Herman1, Neelroop Parikshak1, Maged Goubran2,3, and Jonathan Marchini1
1Regeneron Genetics Center, Tarrytown, NY, United States, 2Sunnybrook Research Institute, Toronto, ON, Canada, 3University of Toronto, Toronto, ON, Canada

Synopsis

Keywords: Segmentation, AI/ML Software, Segmentation, WMH, uncertainty

Motivation: Uncertainty is critical in making informed decisions from the results of machine learning models, however accurately assessing uncertainty relies on the calibration of the underlying model.

Goal(s): To improve the performance and calibration of a white matter hyperintensity segmentation tool.

Approach: Inductive Venn-Abers predictors were used which guarantees good model calibration performance subject to reasonable assumptions. We leveraged an open-source dataset for calibration and testing.

Results: Substantial improvement in calibration metrics were demonstrated, with log-loss being halved and near-perfect calibration being obtained when the assumption of exchangeability was met. Furthermore, the calibrated method demonstrated improved post-threshold performance and a reduction in volumetric bias.

Impact: We demonstrated that Inductive Venn-Abers Predictors can be used to reliably calibrate a deep-learning segmentation tool, which improved model performance, calibration, uncertainty estimates, and aids in the interpretability of the resulting segmentation maps

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Keywords