Global-local transformer for brain age estimation
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 …
imaging (MRI). However, most studies use one neural network to extract the global …
[HTML][HTML] Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review
Y Wu, H Gao, C Zhang, X Ma, X Zhu, S Wu, L Lin - Tomography, 2024 - mdpi.com
The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker
reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine …
reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine …
Understanding the brain with attention: A survey of transformers in brain sciences
Owing to their superior capabilities and advanced achievements, Transformers have
gradually attracted attention with regard to understanding complex brain processing …
gradually attracted attention with regard to understanding complex brain processing …
Brain connectivity based graph convolutional networks and its application to infant age prediction
Infancy is a critical period for the human brain development, and brain age is one of the
indices for the brain development status associated with neuroimaging data. The difference …
indices for the brain development status associated with neuroimaging data. The difference …
Deep relation learning for regression and its application to brain age estimation
Most deep learning models for temporal regression directly output the estimation based on
single input images, ignoring the relationships between different images. In this paper, we …
single input images, ignoring the relationships between different images. In this paper, we …
Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data
Radiation exposure in positron emission tomography (PET) imaging limits its usage in the
studies of radiation-sensitive populations, eg, pregnant women, children, and adults that …
studies of radiation-sensitive populations, eg, pregnant women, children, and adults that …
AlexNet approach for early stage Alzheimer's disease detection from MRI brain images
LS Kumar, S Hariharasitaraman… - Materials Today …, 2022 - Elsevier
Alzheimer's is a neurodegenerative disorder that affects the brain and cognitive function.
Early finding of Alzheimer's disorder of the beginning stages it means Mild Cognitive …
Early finding of Alzheimer's disorder of the beginning stages it means Mild Cognitive …
Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study
Background Biological ageing markers are useful to risk stratify morbidity and mortality more
precisely than chronological age. In this study, we aimed to develop a novel deep-learning …
precisely than chronological age. In this study, we aimed to develop a novel deep-learning …
Semi-supervised contrastive learning for deep regression with ordinal rankings from spectral seriation
Contrastive learning methods can be applied to deep regression by enforcing label distance
relationships in feature space. However, these methods are limited to labeled data only …
relationships in feature space. However, these methods are limited to labeled data only …
Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population
Aging is an important risk factor for disease, leading to morphological change that can be
assessed on Computed Tomography (CT) scans. We propose a deep learning model for …
assessed on Computed Tomography (CT) scans. We propose a deep learning model for …