Comparison of CNN Visualization Methods to Aid Model Interpretability for Detecting Alzheimerxe2x80x99s Disease Advances in medical imaging and convolutional neural networks (CNNs) have made it possible to achieve excellent diagnostic accuracy from CNNs comparable to human raters. However, CNNs are still not implemented in medical trials as they appear as a black box system and their inner workings cannot be properly explained. Therefore, it is essential to assess CNN relevance maps, which highlight regions that primarily contribute to the prediction. This study focuses on the comparison of algorithms for generating heatmaps to visually explain the learned patterns of Alzheimerxe2x80x99s disease (AD) classification. T1weighted volumetric MRI data were entered into a 3D CNN. Heatmaps were then generated for different visualization methods using the iNNvestigate and kerasvis libraries. The model reached an area under the curve of 0.93 and 0.75 for separating AD dementia patients from controls and patients with amnestic mild cognitive impairment from controls, respectively. Visualizations for the methods deep Taylor decomposition and layerwise relevance propagation (LRP) showed most reasonable results for individual patients matching expected brain regions. Other methods, such as GradCAM and guided backpropagation showed more scattered activations or random areas. For clinically research, deep Taylor decomposition and LRP showed most valuable network activation patterns.

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification Over the past decade, machine learning gained considerable attention from the scientific community and has progressed rapidly as a result. Given its ability to detect subtle and complicated patterns, deep learning (DL) has been utilized widely in neuroimaging studies for medical data analysis and automated diagnostics with varying degrees of success. In this paper, we question the remarkable accuracies of the best performing models by assessing generalization performance of the stateoftheart convolutional neural network (CNN) models on the classification of two most common neurodegenerative diseases, namely Alzheimeru0027s Disease (AD) and Parkinsonu0027s Disease (PD) using MRI. We demonstrate the impact of the data division strategy on the model performances by comparing the results derived from two different split approaches. We first evaluated the performance of the CNN models by dividing the dataset at the subject level in which all of the MRI slices of a patient are put into either training or test set. We then observed that pooling together all slices prior to applying crossvalidation, as erroneously done in a number of previous studies, leads to inflated accuracies by as much as 26% for the classification of the diseases.