title: Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease
Creators: name: Dyrba, Martin
name: Hanzig, Moritz
name: Altenstein, Slawek
name: Bader, Sebastian
name: Ballarini, Tommaso
name: Brosseron, Frederic
name: Buerger, Katharina
name: Cantré, Daniel
name: Dechent, Peter
name: Dobisch, Laura
name: Düzel, Emrah
name: Ewers, Michael
name: Fliessbach, Klaus
name: Glanz, Wenzel
name: Haynes, John-Dylan
name: Heneka, Michael T.
name: Janowitz, Daniel
name: Keles, Deniz B.
name: Kilimann, Ingo
name: Laske, Christoph
name: Maier, Franziska
name: Metzger, Coraline D.
name: Munk, Matthias H.
name: Perneczky, Robert
name: Peters, Oliver
name: Preis, Lukas
name: Priller, Josef
name: Rauchmann, Boris
name: Roy, Nina
name: Scheffler, Klaus
name: Schneider, Anja
name: Schott, Björn H.
name: Spottke, Annika
name: Spruth, Eike J.
name: Weber, Marc-André
name: Ertl-Wagner, Birgit
name: Wagner, Michael
name: Wiltfang, Jens
name: Jessen, Frank
name: Teipel, Stefan J.
Publisher: name: BioMed Central
Year: 2024

description: Background: Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge.

Methods: We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection.

Results: Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p &lt; 0.001).

Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.