most citedUncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice

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cs.LG2023★ 1 cited

Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice

Lisa Jöckel, Michael Kläs, Georg Popp +2

When systems use data-based models that are based on machine learning (ML), errors in their results cannot be ruled out. This is particularly critical if it remains unclear to the…