Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

Technological advances have made it possible to study a patient from multiple angles with
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …

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 rapid development of diagnostic technologies in healthcare is leading to higher
requirements for physicians to handle and integrate the heterogeneous, yet complementary …

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

With the wide deployments of heterogeneous networks, huge amounts of data with
characteristics of high volume, high variety, high velocity, and high veracity are generated …

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 …

As large amounts of heterogeneous biomedical data become available, numerous methods
for integrating such datasets have been developed to extract complementary knowledge …

Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

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