Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
Advancements in deep learning techniques carry the potential to make significant
contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis …
contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis …
Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey
Digital twin (DT), referring to a promising technique to digitally and accurately represent
actual physical entities, has attracted explosive interests from both academia and industry …
actual physical entities, has attracted explosive interests from both academia and industry …
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 …
An innovative deep anomaly detection of building energy consumption using energy time-series images
Deep anomaly detection (DAD) is essential in optimizing building energy management.
Nonetheless, most existing works concerning this field consider unsupervised learning and …
Nonetheless, most existing works concerning this field consider unsupervised learning and …
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
J Wen, E Thibeau-Sutre, M Diaz-Melo… - Medical image …, 2020 - Elsevier
Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
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 …
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries.
From a research point of view, impressive results have been reported using computer-aided …
From a research point of view, impressive results have been reported using computer-aided …
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Healthcare data are inherently multimodal, including electronic health records (EHR),
medical images, and multi-omics data. Combining these multimodal data sources …
medical images, and multi-omics data. Combining these multimodal data sources …
Multimodal multitask deep learning model for Alzheimer's disease progression detection based on time series data
Early prediction of Alzheimer's disease (AD) is crucial for delaying its progression. As a
chronic disease, ignoring the temporal dimension of AD data affects the performance of a …
chronic disease, ignoring the temporal dimension of AD data affects the performance of a …