[HTML][HTML] A systematic review of data fusion techniques for optimized structural health monitoring
Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
Deep learning based multimodal biomedical data fusion: An overview and comparative review
Multimodal biomedical data fusion plays a pivotal role in distilling comprehensible and
actionable insights by seamlessly integrating disparate biomedical data from multiple …
actionable insights by seamlessly integrating disparate biomedical data from multiple …
Prompt engineering for healthcare: Methodologies and applications
Prompt engineering is a critical technique in the field of natural language processing that
involves designing and optimizing the prompts used to input information into models, aiming …
involves designing and optimizing the prompts used to input information into models, aiming …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
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 …
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 …
[HTML][HTML] Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion
Deep learning (DL) in orthopaedics has gained significant attention in recent years.
Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks …
Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks …
BrainNet: optimal deep learning feature fusion for brain tumor classification
Early detection of brain tumors can save precious human life. This work presents a fully
automated design to classify brain tumors. The proposed scheme employs optimal deep …
automated design to classify brain tumors. The proposed scheme employs optimal deep …
Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound
Purpose The ultrasound (US) diagnosis of breast cancer is usually based on a single-region
of a whole breast tumor from a single ultrasonic modality, which limits the diagnostic …
of a whole breast tumor from a single ultrasonic modality, which limits the diagnostic …