Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification
This work addresses Alzheimer's Disease diagnosis for clinicians by proposing an interpretable multimodal approach, though it is incremental as it builds on existing part-prototype networks without performance gains.
The authors tackled Alzheimer's Disease classification by developing PIMPNet, an interpretable multimodal model using 3D sMRI and patient age, but it did not improve predictive performance compared to single-modality models.
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