Automated quality monitoring for more reliable integration of neural networks 
into medical workflows

Niehaus, Sebastian

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Automated quality monitoring for more reliable integration of neural networks into medical workflows

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Niehaus, Sebastian
Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Niehaus, S. (2022). Automated quality monitoring for more reliable integration of neural networks into medical workflows. Talk presented at AI.Lounge (Data Science / AI in Lifescience). Leipzig, Germany. 2022-04-26.

Cite as: https://hdl.handle.net/21.11116/0000-000B-1B5D-0

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.