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NeuroMoE++: Patient-Adaptive Multi-Level Multimodal Fusion With Mixture-of-Experts for Neurological Disorder

Wajih Hassan Raza, Yu Wen, Aamir Bader Shah

IEEE Transactions on Bio-Medical Engineering

|May 13, 2026

Summary

This summary is machine-generated.

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NeuroMoE++ enhances neurological disorder diagnosis by integrating multiple data types early. This novel approach significantly improves accuracy by capturing subtle cross-modality patterns for better patient-specific decisions.

Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Early diagnosis of neurological disorders (NDs) relies on diverse data like MRI and biomarkers.
Current multimodal learning often uses late fusion, missing crucial cross-modality interactions.
Subtle patterns in early disease stages are underutilized by existing methods.

Purpose of the Study:

To introduce NeuroMoE++, a hierarchical framework for explicit cross-modality interaction in neurological diagnosis.
To improve the utilization of complementary information from different diagnostic signals.
To enable patient-specific diagnostic decisions through adaptive integration.

Main Methods:

Developed NeuroMoE++, a hierarchical end-to-end framework for multimodal learning.

Related Experiment Videos

Enabled explicit interaction across modalities during feature extraction.

Implemented subject-driven adaptive integration for patient-specific decisions.

Main Results:

NeuroMoE++ achieved 84.94% accuracy on real clinical datasets.
The model significantly outperformed unimodal and late-fusion baseline methods.
Demonstrated the effectiveness of explicit cross-modality reasoning.

Conclusions:

NeuroMoE++ offers a more effective approach to neurological disorder diagnosis.
Explicit cross-modality reasoning is valuable for capturing subtle diagnostic patterns.
The framework provides interpretable, patient-specific decisions for clinical application.