# NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification

arXiv: https://arxiv.org/abs/2506.14970 · HTML: https://mlsanity.com/paper/2506.14970/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts.
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Placed in

- Mixture-of-experts / [Medical and biomedical MoE](https://mlsanity.com/moe/medical-and-biomedical-moe/)

## Builds on (most cited first, 6 of 6)

- [Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts](https://mlsanity.com/paper/2410.08245/) (2024)
- [Towards Understanding Mixture of Experts in Deep Learning](https://mlsanity.com/paper/2208.02813/) (2022)
- [Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models](https://mlsanity.com/paper/2404.10237/) (2024)
- [Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2206.08826/) (2022)
- [Dynamic Image for 3D MRI Image Alzheimer's Disease Classification](https://mlsanity.com/paper/2012.00119/) (2020)
- [A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Networks](https://mlsanity.com/paper/1904.07950/) (2019)

## Built on by (most cited first, 5 of 5)

- [Benchmarking and Enhancing PPG-Based Cuffless Blood Pressure Estimation Methods](https://mlsanity.com/paper/2602.04725/) (2026)
- [Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networks](https://mlsanity.com/paper/2603.13497/) (2026)
- [DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals](https://mlsanity.com/paper/2606.11125/) (2026)
- [Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation](https://mlsanity.com/paper/2606.17405/) (2026)
- [Lightweight Chunk Selection for Mobile Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.03148/) (2026)

## Abstract (arXiv)

The integration of multi-modal Magnetic Resonance Imaging (MRI) and clinical data holds great promise for enhancing the diagnosis of neurological disorders (NDs) in real-world clinical settings. Deep Learning (DL) has recently emerged as a powerful tool for extracting meaningful patterns from medical data to aid in diagnosis. However, existing DL approaches struggle to effectively leverage multi-modal MRI and clinical data, leading to suboptimal performance. To address this challenge, we utilize a unique, proprietary multi-modal clinical dataset curated for ND research. Based on this dataset, we propose a novel transformer-based Mixture-of-Experts (MoE) framework for ND classification, leveraging multiple MRI modalities-anatomical (aMRI), Diffusion Tensor Imaging (DTI), and functional (fMRI)-alongside clinical assessments. Our framework employs transformer encoders to capture spatial relationships within volumetric MRI data while utilizing modality-specific experts for targeted feature extraction. A gating mechanism with adaptive fusion dynamically integrates expert outputs, ensuring optimal predictive performance. Comprehensive experiments and comparisons with multiple baselines demonstrate that our multi-modal approach significantly enhances diagnostic accuracy, particularly in distinguishing overlapping disease states. Our framework achieves a validation accuracy of 82.47%, outperforming baseline methods by over 10%, highlighting its potential to improve ND diagnosis by applying multi-modal learning to real-world clinical data.