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Efficient Slice-Patch Selection Transformer for Interpretable Alzheimer's Disease Diagnosis Using Structural MRI.

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    This summary is machine-generated.

    This study introduces SPSFormer, an efficient AI model for Alzheimer's disease (AD) screening using brain MRI. It reduces computational cost and improves interpretability for clinical use.

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    Area of Science:

    • Neuroimaging
    • Artificial Intelligence
    • Medical Diagnostics

    Background:

    • Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) detection.
    • Vision Transformers (ViTs) show promise for sMRI analysis but face computational and interpretability challenges in clinical settings.

    Purpose of the Study:

    • To develop an efficient and interpretable AI framework for sMRI-based AD diagnosis.
    • To reduce the computational overhead and enhance the clinical applicability of ViTs for AD screening.

    Main Methods:

    • Proposed the slice-patch selection Transformer (SPSFormer) framework, which selectively focuses on relevant sMRI slices and patches.
    • Utilized lightweight learnable scorers and a differentiable Top-k operator for efficient, end-to-end training.
    • Validated the model across multiple datasets (NACC, ADNI, AIBL) using DeiT and Swin recognizers.

    Main Results:

    • SPSFormer reduced computational requirements (GFLOPs) by 2-4x compared to existing ViTs while maintaining diagnostic accuracy.
    • Identified key brain regions (hippocampus, amygdala, thalamus) associated with AD neuropathology, enhancing model interpretability.
    • Demonstrated neurobiological validity through associations with cognitive/biomarker measures and prognostic value for predicting conversion from mild cognitive impairment to AD.

    Conclusions:

    • SPSFormer offers a computationally efficient and interpretable AI solution for AD detection using sMRI.
    • The framework's blend of efficiency and explainability paves the way for trustworthy, clinically deployable AI in AD diagnostics.
    • Highlights the potential of targeted feature selection in deep learning for medical imaging analysis.