Abstract
One of the major research fields in medical applications is Computer-aided dementia diagnosis since it progressively declines the cognitive function and afflicts millions of people worldwide, becoming a leading cause of mortality and morbidity of elder people. Pattern recognition methods, applied to dementia diagnosis, improve either the feature extraction or the classifier stage. Particularly, deep learning machines have raised attention to clinical applications since they work in both stages to enhance the system performance. However, the architecture of these machines is highly complex, making hard their training procedures. In this work, we propose a deep supervised feature extraction approach using General Stochastic Networks through a supervised layer-wise non-linear mapping learning. To this end, we maximize the centered kernel alignment function, which accounts for the provided discriminative information regarding the projection of each layer of the network. Our proposal improves the classifier performance by highlighting the class discrimination during the training stage. Besides, we provide a non-linear relevance measure assessing the contribution of the input feature set to build each latent space which is related to the clinical knowledge. Comparison against other automated diagnosis approaches using different features and classification machines is presented for multi-class and bi-class scenarios on the widely-known ADNI database. As a result, our proposal outperforms the compared approaches, reduces the class biasing, and enhances clinical interpretability.
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This work was supported by the research project 111974455497 founded by COLCIENCIAS.
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Collazos-Huertas, D., Tobar-Rodriguez, A., Cárdenas-Peña, D., Castellanos-Dominguez, G. (2017). MRI-Based Feature Extraction Using Supervised General Stochastic Networks in Dementia Diagnosis. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_36
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DOI: https://doi.org/10.1007/978-3-319-59740-9_36
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