  Information from neuroimaging examinations is increasingly used to support
diagnoses of dementia, e.g., Alzheimer's disease. While current clinical
practice is mainly based on visual inspection and feature engineering, Deep
Learning approaches can be used to automate the analysis and to discover new
image-biomarkers. Part-prototype neural networks (PP-NN) are an alternative to
standard blackbox models, and have shown promising results in general computer
vision. PP-NN's base their reasoning on prototypical image regions that are
learned fully unsupervised, and combined with a simple-to-understand decision
layer. We present PIPNet3D, a PP-NN for volumetric images. We apply PIPNet3D to
the clinical diagnosis of Alzheimer's Disease from structural Magnetic
Resonance Imaging (sMRI). We assess the quality of prototypes under a
systematic evaluation framework, propose new functionally grounded metrics to
evaluate brain prototypes and develop an evaluation scheme to assess their
coherency with domain experts. Our results show that PIPNet3D is an
interpretable, compact model for Alzheimer's diagnosis with its reasoning well
aligned to medical domain knowledge. Notably, PIPNet3D achieves the same
accuracy as its blackbox counterpart; and removing the remaining clinically
irrelevant prototypes from its decision process does not decrease predictive
performance.
