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Visualization of deep learning relevance maps for AD detection

Deep learning methods, in particular convolutional neural networks (CNNs), have been very successfully applied to the diagnosis of AD and MCI based on structural MRI data. However, since the training of those models needs the adaptation of many parameters, they are often criticized for being a “black‐box”. Here, we compute heatmaps using layer‐wise relevance propagation (LRP) to (1) validate CNN models and (2) explain neural network decision in single patients. Furthermore, we investigated whether the resulting heatmaps help in identifying subgroups of AD and MCI.