ArticleFrontiers in neuroscience2017
Enhanced Data Representation by Kernel Metric Learning for Dementia Diagnosis.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Data Leakage in Deep Learning for Alzheimer's Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation.Diagnostics (Basel, Switzerland) · 2025Review
- Combined impact of neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) and cognitive function on all-cause mortality in older adults: a population-based study.Lipids in health and disease · 2025Article
- Imaging biomarkers in neurodegeneration: current and future practices.Alzheimer's research & therapy · 2020Review
- Diagnosis of Alzheimer's Disease via Multi-Modality 3D Convolutional Neural Network.Frontiers in neuroscience · 2019Article
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Authors and funding
3 authors.
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Abstract
Alzheimer's disease (AD) is the kind of dementia that affects the most people around the world. Therefore, an early identification supporting effective treatments is required to increase the life quality of a wide number of patients. Recently, computer-aided diagnosis tools for dementia using Magnetic Resonance Imaging scans have been successfully proposed to discriminate between patients with AD, mild cognitive impairment, and healthy controls. Most of the attention has been given to the clinical data, provided by initiatives as the ADNI, supporting reliable researches on intervention, prevention, and treatments of AD. Therefore, there is a need for improving the performance of classification machines. In this paper, we propose a kernel framework for learning metrics that enhances conventional machines and supports the diagnosis of dementia. Our framework aims at building discriminative spaces through the maximization of center kernel alignment function, aiming at improving the discrimination of the three considered neurological classes. The proposed metric learning performance is evaluated on the widely-known ADNI database using three supervised classification machines (
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