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Abstract:
The quality of neural network predictions is highly dependent on the quality and consistency of the input data. If there is too much discrepancy between the training data and the data on which the neural network is applied, the prediction quality decreases. In clinical applications, this often leads to problems, as each prediction has to be checked manually. We present an alternative approach for automatic quality monitoring.
