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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