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Abstract #4813

Relevance-guided Feature Extraction for Alzheimer's Disease Classification

Christian Tinauer1, Stefan Heber1, Lukas Pirpamer1, Anna Damulina1, Maximilian Sackl1, Edith Hofer1, Marisa Koini1, Reinhold Schmidt1, Stefan Ropele1, and Christian Langkammer1

1Department of Neurology, Medical University of Graz, Graz, Austria

Using FLAIR images we separated Alzheimer's patients (n=106) from controls (n=173) by using a deep convolutional neural network and found that the classifier might learn irrelevant features e.g. outside the brain. Preprocessing of MRI plays a crucial but often neglected role in classification and therefore we have developed a method enforcing the relevant features to be within brain tissue and, thus, eliminated the influence of precomputed brain masks. While our relevance-guided training method reached the same classification accuracy, incorporating relevance improved feature identification in an anatomically more reasonable manner.

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