The nature of statistical learning theory
The nature of statistical learning theory
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Classification of SPECT Images of Normal Subjects versus Images of Alzheimer's Disease Patients
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Numerical Recipes: The Art of Scientific Computing with IBM PC or Macintosh
Numerical Recipes: The Art of Scientific Computing with IBM PC or Macintosh
Optimal feature selection for support vector machines
Pattern Recognition
A case study of stacked multi-view learning in dementia research
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Partial least squares for feature extraction of SPECT images
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Information Sciences: an International Journal
Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps
Computers in Biology and Medicine
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease
Pattern Recognition Letters
A fast algorithm for kernel 1-norm support vector machines
Knowledge-Based Systems
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Alzheimer's disease is the most frequent type of dementia for elderly patients. Due to aging populations the occurrence of this disease will increase in the next years. Early diagnosis is crucial to be able to develop more powerful treatments. Brain perfusion changes can be a marker for Alzheimer's disease. In this article we study the use of SPECT perfusion imaging for the diagnosis of Alzheimer's disease differentiating between images from healthy subjects and images from Alzheimer's disease patients. Our classification approach is based on a linear programming formulation similar to the 1-norm support vector machines. In contrastwith other linear hyperplane-based methods that perform simultaneous feature selection and classification, our proposed formulation incorporates proximity information about the features and generates a classifier that does not just select the most relevant voxels but the most relevant "areas" for classification resulting in more robust classifiersthat are better suitable for interpretation. This approach is compared with the classical Fisher linear discriminant (FLD) classifier as well as with statistical parametric mapping (SPM). We tested our method on data from four European institutions. Our method achieved sensitivity of 84.4% at 90.9% specificity, this is considerable better the human experts. Our method also outperformed the FLD and SPM techniques. We conclude that our approach has the potential to be a useful help for clinicians.