Making AI more trustworthy (IMAGE)
Caption
A neural network is initially trained with many data sets in order to be able to distinguish tumour-containing from tumour-free tissue images (input from the top in the diagram). It is then presented with a new tissue image from an experiment (input from the left). Via inductive reasoning, the neural network generates the classification “tumour-containing” or “tumour-free” for the respective image. At the same time, it creates an activation map of the tissue image. The activation map has emerged from the inductive learning process and is initially unrelated to reality. The correlation is established by the falsifiable hypothesis that areas with high activation correspond exactly to the tumour regions in the sample. This hypothesis can be tested with further experiments. This means that the approach follows deductive logic.
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PRODI
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The image may only be used for reporting about the research by Axel Mosig at Ruhr-Universität Bochum.
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