Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Remarkable progress in ageing research has been achieved over the past decades.
General perceptions and experimental evidence pinpoint that the decline of physical …

Biological aging of human organ systems reflects the interplay of age, chronic disease,
lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes …

Precision medicine is an emerging approach to clinical research and patient care that
focuses on understanding and treating disease by integrating multi-modal or multi-omics …

Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

Deep learning has emerged as a powerful approach to constructing imaging signatures of
normal brain ageing as well as of various neuropathological processes associated with …

Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …

Deep learning can provide rapid brain age estimation based on brain magnetic resonance
imaging (MRI). However, most studies use one neural network to extract the global …

Medical datasets and especially biobanks, often contain extensive tabular data with rich
clinical information in addition to images. In practice, clinicians typically have less data, both …

Brain aging is accompanied by patterns of functional and structural change. Alzheimer's
disease (AD), a representative neurodegenerative disease, has been linked to accelerated …