# Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification

arXiv: https://arxiv.org/abs/2503.01232 · HTML: https://mlsanity.com/paper/2503.01232/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 3 Mar 2025.

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

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes](https://mlsanity.com/paper/1905.08627/) (2019)

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

- [Residual-Guided Expert Specialization for Incomplete Multimodal Learning](https://mlsanity.com/paper/2606.30355/) (2026)
- [ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs](https://mlsanity.com/paper/2606.30398/) (2026)

## Abstract (arXiv)

Stacking excessive layers in DNN results in highly underdetermined system when training samples are limited, which is very common in medical applications. In this regard, we present a framework capable of deriving an efficient high-dimensional space with reasonable increase in model size. This is done by utilizing a transform (i.e., convolution) that leverages scale-space theory with covariance structure. The overall model trains on this transform together with a downstream classifier (i.e., Fully Connected layer) to capture the optimal multi-scale representation of the original data which corresponds to task-specific components in a dual space. Experiments on neuroimaging measures from Alzheimer's Disease Neuroimaging Initiative (ADNI) study show that our model performs better and converges faster than conventional models even when the model size is significantly reduced. The trained model is made interpretable using gradient information over the multi-scale transform to delineate personalized AD-specific regions in the brain.