5 citations · 7 across the 3 of their papers we have counts for
3 papers
Metric Hub: A metric library and practical selection workflow for use-case-driven data quality assessment in medical AI
Katinka Becker, Maximilian P. Oppelt, Tobias S. Zech +19
Machine learning (ML) in medicine has transitioned from research to concrete applications aimed at supporting several medical purposes like therapy selection, monitoring and treatm…
The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review
Daniel Schwabe, Katinka Becker, Martin Seyferth +2
The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is es…
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift
Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz +4
For many applications, analyzing the uncertainty of a machine learning model is indispensable. While research of uncertainty quantification (UQ) techniques is very advanced for com…