Advancements in deep learning techniques carry the potential to make significant
contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis�…

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

In recent years, deep learning has been applied in the field of clinical medicine to process
large-scale medical images, for large-scale data screening, and in the diagnosis and�…

Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability�…

Today's digital health revolution aims to improve the efficiency of healthcare delivery and
make care more personalized and timely. Sources of data for digital health tools include�…

The image fusion methods based on deep learning has become a research hotspot in the
field of computer vision in recent years. This paper reviews these methods from five aspects�…

Background and objectives Over the past two decades, medical imaging has been
extensively apply to diagnose diseases. Medical experts continue to have difficulties for�…

Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby�…

The medical image fusion is the process of coalescing multiple images from multiple
imaging modalities to obtain a fused image with a large amount of information for increasing�…

Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized�…