PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans

TL;DR

PIPNet3D is an interpretable neural network model that accurately diagnoses Alzheimer's from MRI scans by using prototypical image regions, aligning well with medical knowledge and maintaining performance after removing irrelevant prototypes.

Contribution

This paper introduces PIPNet3D, a novel 3D part-prototype neural network for Alzheimer's diagnosis that emphasizes interpretability and prototype evaluation in medical imaging.

Findings

PIPNet3D achieves comparable accuracy to blackbox models.

Prototypes can be removed without affecting performance.

Prototypes align with domain expert knowledge.

Abstract

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.