Multimodal deep learning for Alzheimer's disease dementia assessment
Worldwide, there are nearly 10 million new cases of dementia annually, of which
Alzheimer's disease (AD) is the most common. New measures are needed to improve the …
Alzheimer's disease (AD) is the most common. New measures are needed to improve the …
Development and validation of an interpretable deep learning framework for Alzheimer's disease classification
Alzheimer's disease is the primary cause of dementia worldwide, with an increasing
morbidity burden that may outstrip diagnosis and management capacity as the population …
morbidity burden that may outstrip diagnosis and management capacity as the population …
Multimodal attention-based deep learning for Alzheimer's disease diagnosis
M Golovanevsky, C Eickhoff… - Journal of the American …, 2022 - academic.oup.com
Objective Alzheimer's disease (AD) is the most common neurodegenerative disorder with
one of the most complex pathogeneses, making effective and clinically actionable decision …
one of the most complex pathogeneses, making effective and clinically actionable decision …
Multimodal deep learning models for early detection of Alzheimer's disease stage
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
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 …
A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's
disease (AD), while other MCI types tend to remain stable over-time and do not progress to …
disease (AD), while other MCI types tend to remain stable over-time and do not progress to …
Multimodal inductive transfer learning for detection of Alzheimer's dementia and its severity
Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising
rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost …
rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost …
Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated
morphologies. Standard semi-quantitative scoring approaches, however, are coarse …
morphologies. Standard semi-quantitative scoring approaches, however, are coarse …
Deep learning based pipelines for Alzheimer's disease diagnosis: a comparative study and a novel deep-ensemble method
A Loddo, S Buttau, C Di Ruberto - Computers in biology and medicine, 2022 - Elsevier
Background Alzheimer's disease is a chronic neurodegenerative disease that destroys brain
cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are …
cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are …
A multi-modal convolutional neural network framework for the prediction of Alzheimer's disease
This paper presents a multi-modal Alzheimer's disease (AD) classification framework based
on a convolutional neural network (CNN) architecture. The devised model takes structural …
on a convolutional neural network (CNN) architecture. The devised model takes structural …