Deep Learning has introduced the possibility to speed up quantitation in Magnetic Resonance Spectroscopy. However, questions arise about how to access and relate to prediction uncertainties. Distributions of predictions and Monte-Carlo dropout are here used to investigate data and model related uncertainties, exploiting ground truth knowledge (in-silico set up). It is confirmed that DL is a dataset-biased technique, showing higher uncertainties toward the edges of its training set. Surprisingly, metabolites present in high concentrations suffer from comparable high uncertainties as when present in low concentrations. Evaluating and respecting fitting uncertainties is equally crucial for DL and traditional approaches.
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