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
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords