# Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease

arXiv: https://arxiv.org/abs/2507.20872 · HTML: https://mlsanity.com/paper/2507.20872/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts](https://mlsanity.com/paper/2410.08245/) (2024)
- [Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification](https://mlsanity.com/paper/2106.08808/) (2021)
- [Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2206.08826/) (2022)
- [Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data](https://mlsanity.com/paper/2210.00255/) (2022)
- [Anatomical Foundation Models for Brain MRIs](https://mlsanity.com/paper/2408.07079/) (2024)
- [MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study](https://mlsanity.com/paper/2411.04155/) (2024)
- [ITCFN: Incomplete Triple-Modal Co-Attention Fusion Network for Mild Cognitive Impairment Conversion Prediction](https://mlsanity.com/paper/2501.11276/) (2025)

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

- [Vesta: A Generalist Embodied Reasoning Model](https://mlsanity.com/paper/2606.20905/) (2026)
- [A Multimodal Explainable Deep Learning Framework for Alzheimer's Disease Diagnosis using 3D Magnetic Resonance Imaging and Clinical Data](https://mlsanity.com/paper/2609.12410/) (2026)

## Abstract (arXiv)

Alzheimer's disease affects over 55 million people worldwide and is projected to more than double by 2050, necessitating rapid, accurate, and scalable diagnostics. However, existing approaches are limited because they cannot achieve clinically acceptable accuracy, generalization across datasets, robustness to missing modalities, and explainability all at the same time. This inability to satisfy all these requirements simultaneously undermines their reliability in clinical settings. We propose OmniBrain, a multimodal framework that integrates brain MRI, radiomics, gene expression, and clinical data using a unified model with cross-attention and modality dropout. OmniBrain achieves 92.2 ± 2.4%accuracy on the ANMerge dataset and generalizes to the MRI-only ADNI dataset with 70.4 ± 2.7% accuracy, outperforming unimodal and prior multimodal approaches. Explainability analyses highlight neuropathologically relevant brain regions and genes, enhancing clinical trust. OmniBrain offers a robust, interpretable, and practical solution for real-world Alzheimer's diagnosis.