[HTML][HTML] Multi-modality approaches for medical support systems: A systematic review of the last decade
Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …
available for medical decisions. However, advancements in technology and the availability …
Advances in multimodal data fusion in neuroimaging: overview, challenges, and novel orientation
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and
progression detection have been intensively studied. Nevertheless, research studies often …
progression detection have been intensively studied. Nevertheless, research studies often …
Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
Predicting Alzheimer's disease progression using multi-modal deep learning approach
Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline
in cognitive functions with no validated disease modifying treatment. It is critical for timely …
in cognitive functions with no validated disease modifying treatment. It is critical for timely …
Dual attention multi-instance deep learning for Alzheimer's disease diagnosis with structural MRI
Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological
disease diagnosis, which could reflect the variations of brain. However, due to the local …
disease diagnosis, which could reflect the variations of brain. However, due to the local …
Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from
using traditional univariate brain map** approaches to multivariate predictive models …
using traditional univariate brain map** approaches to multivariate predictive models …
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …