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

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 numer- ous efforts have been devoted to improve MRI-based feature quality or to increase machine learning methods accuracy, the current AD prog- nosis accuracy remains limited. In this paper, we propose to combine both high quality biomarkers and advanced learning method. Our ap- proach is based on a robust ensemble learning strategy using gray matter grading. The estimated weak classifiers are then fused into high infor- mative 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%.
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Communication dans un congrès
5th International Workshop on Machine Learning in Medical Imaging, Sep 2014, United States. 8 p., 2014
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Dernière modification le : mardi 28 octobre 2014 - 18:57:28
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Mawulawoé Komlagan, Vinh Thong Ta, Xingyu Pan, Jean-Philippe Domenger, D. Louis Collins, et al.. Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer's Disease. 5th International Workshop on Machine Learning in Medical Imaging, Sep 2014, United States. 8 p., 2014. <hal-01062759>

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