Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …

Background The improvement of health indicators and life expectancy, especially in
developed countries, has led to population growth and increased age-related diseases …

Rational Deep learning (DL) has demonstrated a remarkable performance in diagnostic
imaging for various diseases and modalities and therefore has a high potential to be used …

Identifying reproducible and interpretable biomarkers for Alzheimer's disease (AD) detection
remains a challenge. AD detection using multi-center datasets can expand the sample size …

Abstract Machine learning brings the hope of finding new biomarkers extracted from cohorts
with rich biomedical measurements. A good biomarker is one that gives reliable detection of …

Introduction Machine learning research into automated dementia diagnosis is becoming
increasingly popular but so far has had limited clinical impact. A key challenge is building …

Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the
growing prevalence of the disease and the resulting costs imposed on individuals and …

In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets
leads to the degraded performance of models in the target sites. The traditional domain …

Visual classification for medical images has been dominated by convolutional neural
networks (CNNs) for years. Though they have shown great performance on accuracy, some …

Individuals with mild cognitive impairment (MCI) of different subtypes show distinct
alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by …