Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease
Authors: Dyrba Martin , Hanzig Moritz , Altenstein Slawek , Bader Sebastian , Ballarini Tommaso , Brosseron Frederic , Buerger Katharina , Cantré Daniel , Dechent Peter , Dobisch Laura , Düzel Emrah , Ewers Michael , Fliessbach Klaus , Glanz Wenzel , Haynes John-Dylan , Heneka Michael T. , Janowitz Daniel , Keles Deniz B. , Kilimann Ingo , Laske Christoph , Maier Franziska , Metzger Coraline D. , Munk Matthias H. , Perneczky Robert , Peters Oliver , Preis Lukas , Priller Josef , Rauchmann Boris , Roy Nina , Scheffler Klaus , Schneider Anja , Schott Björn H. , Spottke Annika , Spruth Eike J. , Weber Marc-André , Ertl-Wagner Birgit , Wagner Michael , Wiltfang Jens , Jessen Frank , Teipel Stefan J. ,   for the ADNI, AIBL, DELCODE study groups
Published: November 2021

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 < 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.