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%.
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Komlagan, M. et al. (2014). Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_18
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DOI: https://doi.org/10.1007/978-3-319-10581-9_18
Publisher Name: Springer, Cham
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