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Beyond Performance Metrics: Uncertainty Mapping of Label Ambiguity in Fazekas Score Prediction

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Reference labels used to train medical image classification models are not always as certain as they may appear, and this uncertainty has implications on performance metrics. In this study, we propose a framework to analyze model performance for periventricular Fazekas score prediction that goes beyond conventional metrics. The Fazekas score is an ordinal visual rating scale used to assess the severity of white matter hyperintensities and is known to be affected by inter-rater variability. While the best Fazekas score prediction model achieved a Matthews correlation coefficient (MCC) of 0.70,

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