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Related Experiment Video

Updated: Apr 19, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification.

Rui Min, Jian Cheng, True Price

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |December 9, 2014
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    Summary
    This summary is machine-generated.

    This study introduces a novel maximum-margin based representation learning (MMRL) method for Alzheimer's disease (AD) analysis. The approach effectively distinguishes between Alzheimer's patients and healthy controls using multiple brain atlases.

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    Area of Science:

    • Neuroimaging
    • Machine Learning
    • Biomedical Data Analysis

    Background:

    • Spatial normalization is common for Alzheimer's disease (AD) brain analysis.
    • Comparing subjects in a single atlas space may limit the detection of complex brain changes.

    Purpose of the Study:

    • To develop a novel method for improved Alzheimer's disease (AD) classification.
    • To learn optimal brain representations from multiple atlases for enhanced discrimination.

    Main Methods:

    • Proposed a maximum-margin based representation learning (MMRL) method.
    • Learned representations jointly with a classification model, unlike traditional separate approaches.
    • Utilized multiple atlases for feature extraction instead of a single atlas.

    Main Results:

    • Achieved 90.69% accuracy for Alzheimer's disease (AD) versus normal control (NC) classification.
    • Attained 73.69% accuracy for progressive mild cognitive impairment (p-MCI) versus stable mild cognitive impairment (s-MCI) classification.
    • Demonstrated superior discrimination power by learning representations and classification simultaneously.

    Conclusions:

    • The proposed MMRL method enhances the discrimination of Alzheimer's disease (AD) patients from normal controls (NC).
    • Joint representation learning and classification improve diagnostic accuracy for AD and subtypes of mild cognitive impairment (MCI).
    • This multi-atlas approach offers a more powerful tool for neuroimaging-based disease analysis.