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Abstract #1472

Relevance-guided Deep Learning for Feature Identification in R2* Maps in Alzheimer’s Disease Classification

Christian Tinauer1, Stefan Heber1, Lukas Pirpamer1, Anna Damulina1, Martin Soellradl1, Maximilian Sackl1, Edith Hofer1, Marisa Koini1, Reinhold Schmidt1, Stefan Ropele1, and Christian Langkammer1
1Medical University of Graz, Graz, Austria

Using R2* maps we separated Alzheimer's patients (n=119) from healthy controls (n=131) by using a deep neural network and found that the preprocessing steps might introduce unwanted features to be used by the classifier. We systematically investigated the influence of registration and brain extraction on the learned features using a relevance map generator attached to the classification network. The results were compared to our relevance-guided training method. While the resulting classification accuracy on the testset was similar for all training configurations, the relevance-guided method identified anatomical regions, which are known to have higher R2* values.

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