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Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease

  • Mawulawoé Komlagan
  • Vinh-Thong Ta
  • Xingyu Pan
  • Jean-Philippe Domenger
  • D. Louis Collins
  • Pierrick Coupé
  • the Alzheimer’s Disease Neuroimaging Initiative
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8679)

Abstract

The early detection of Alzheimer’s disease (AD) is a key step to accelerate the development of new therapies and to diminish the associated socio-economic burden. To address this challenging problem, several biomarkers based on MRI have been proposed. Although numerous efforts have been devoted to improve MRI-based feature quality or to increase machine learning methods accuracy, the current AD prognosis accuracy remains limited. In this paper, we propose to combine both high quality biomarkers and advanced learning method. Our approach is based on a robust ensemble learning strategy using gray matter grading. The estimated weak classifiers are then fused into high informative anatomical sub-ensembles. Through a sparse logistic regression, the most relevant anatomical sub-ensembles are selected, weighted and used as input to a global classifier. Validation on the full ADNI1 dataset demonstrates that the proposed method obtains competitive results of prediction of conversion to AD in the Mild Cognitive Impairment group with an accuracy of 75.6%.

Keywords

Ensemble learning Weak classifier Sparse logistic regression 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mawulawoé Komlagan
    • 1
    • 2
  • Vinh-Thong Ta
    • 1
    • 2
    • 3
  • Xingyu Pan
    • 1
    • 2
  • Jean-Philippe Domenger
    • 1
    • 2
  • D. Louis Collins
    • 4
  • Pierrick Coupé
    • 1
    • 2
  • the Alzheimer’s Disease Neuroimaging Initiative
    • 1
  1. 1.LaBRI, UMR 5800, PICTURAUniv. BordeauxTalenceFrance
  2. 2.CNRS, LaBRI, UMR 5800, PICTURATalenceFrance
  3. 3.IPB, LaBRI, UMR 5800, PICTURAPessacFrance
  4. 4.McConnell Brain Imaging Centre, Montreal Neurological InstituteMcGill UniversityMontrealCanada

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