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Explainable Deep Learning Framework for Multimodal Brain Tumor Classification via Neuroimaging Attribute Extraction.

Long Bao, Shakir Khan, Yimin Wang

    IEEE Journal of Biomedical and Health Informatics
    |May 20, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    We developed MADEX, a multimodal AI framework for brain tumor grading using MRI. It provides accurate, interpretable results aligned with clinical guidelines, enhancing trust in AI for neuro-oncology.

    Area of Science:

    • Artificial Intelligence
    • Neuroimaging
    • Oncology

    Background:

    • Deep learning models for brain tumor grading lack transparency, hindering clinical adoption.
    • Interpretable AI is crucial for accountability in medical decision-making.

    Purpose of the Study:

    • To introduce MADEX, a multimodal attribute-based decision-explanation framework for interpretable brain tumor grading.
    • To align AI-driven explanations with the World Health Organization (WHO) glioma classification structure.

    Main Methods:

    • Utilized convolutional encoders to extract interpretable neuroimaging attributes.
    • Employed an adaptive modality exchange mechanism to handle artifact-corrupted MRI sequences.
    • Applied layer-wise Dempster-Shafer evidential fusion for uncertainty quantification and evidence combination.

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  • Incorporated prototype constraints to ensure clinical consistency with diagnostic criteria.

    • Main Results:

    • Achieved high classification accuracies: 89.7% on BT-large-2c and 87.3% on BRaTS 2021 datasets.
    • Demonstrated superior interpretability with sparse attribute representations (90%+ performance with 10 features).
    • Validation confirmed that learned explanations reflect genuine diagnostic features, not spurious correlations.

    Conclusions:

    • MADEX offers a responsible AI foundation for neuro-oncological decision support.
    • The framework provides accurate, interpretable, and uncertainty-aware AI for brain tumor grading.
    • MADEX aligns with clinical workflows, promoting trust and adoption of AI in neuro-oncology.