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

arXiv: https://arxiv.org/abs/2407.14277 · HTML: https://mlsanity.com/paper/2407.14277/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers](https://mlsanity.com/paper/2307.14517/) (2023)
- [A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments](https://mlsanity.com/paper/1905.09826/) (2019)
- [Multimodality in Meta-Learning: A Comprehensive Survey](https://mlsanity.com/paper/2109.13576/) (2021)
- [Interpreting and Correcting Medical Image Classification with PIP-Net](https://mlsanity.com/paper/2307.10404/) (2023)
- [COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings](https://mlsanity.com/paper/2206.15073/) (2022)
- [PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans](https://mlsanity.com/paper/2403.18328/) (2024)

## Built on by (most cited first, 2 of 2)

- [This looks like what? Challenges and Future Research Directions for Part-Prototype Models](https://mlsanity.com/paper/2502.09340/) (2025)
- [Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning](https://mlsanity.com/paper/2503.08636/) (2025)

## Abstract (arXiv)

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