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 …

Healthcare data are inherently multimodal, including electronic health records (EHR),
medical images, and multi-omics data. Combining these multimodal data sources …

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 …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Multimodal biomedical data fusion plays a pivotal role in distilling comprehensible and
actionable insights by seamlessly integrating disparate biomedical data from multiple …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring
various diseases. Convolutional neural networks (CNNs) have become popular as they can …

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it
combines information from various imaging modalities to provide a more comprehensive …

Purpose Alzheimer's disease (AD) is a progressive, incurable human brain illness that
impairs reasoning and retention as well as recall. Detecting AD in its preliminary stages …

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 …