Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification

Volumetric neuroimaging examinations like structural Magnetic Resonance Imaging (sMRI) are routinely applied to support the clinical diagnosis of dementia like Alzheimer's Disease (AD). Neuroradiologists examine 3D sMRI to detect and monitor abnormalities in brain morphology due to AD, like global and/or local brain atrophy and shape alteration of characteristic structures. There is a strong research interest in developing diagnostic systems based on Deep Learning (DL) models to analyse sMRI for AD. However, anatomical information extracted from an sMRI examination needs to be interpreted together with patient's age to distinguish AD patterns from the regular alteration due to a normal ageing process. In this context, part-prototype neural networks integrate the computational advantages of DL in an interpretable-by-design architecture and showed promising results in medical imaging applications. We present PIMPNet, the first interpretable multimodal model for 3D images and demographics applied to the binary classification of AD from 3D sMRI and patient's age. Despite age prototypes do not improve predictive performance compared to the single modality model, this lays the foundation for future work in the direction of the model's design and multimodal prototype training process

PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans

Information from neuroimaging examinations (CT, MRI) 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 case study of Alzheimer's Disease diagnosis from structural Magnetic Resonance Imaging (sMRI). We assess the quality of prototypes under a systematic evaluation framework, propose new metrics to evaluate brain prototypes and perform an evaluation 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.