U-ProBE: Uncertainty Probabilistic Bayesian Estimate
Bandini L., Del Corso G., Colantonio S., Caudai C.
Probabilistic Deep Learning Bayesian Estimate Uncertainty Quantification Post-hoc Methods
In this technical report we have designed and developed a Python software suite (U-ProBE: Uncertainty Probabilistic Bayesian Estimate) for analyzing Deep Learning models with predictions affected by uncertainty (i.e., Bayesian Probabilistic Models). The suite is equipped with an intuitive graphical interface that is simple to use even for non-experts and designed to support a growing pool of users who need to evaluate a model’s performance and, above all, its uncertainty.
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@misc{oai:iris.cnr.it:20.500.14243/541062,
title = {U-ProBE: Uncertainty Probabilistic Bayesian Estimate},
author = {Bandini L. and Del Corso G. and Colantonio S. and Caudai C.},
doi = {10.32079/isti-tr-2025/006},
year = {2025}
}
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ProCAncer-I
An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum
