[CLOSED] Call for Papers: Special Issue on Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare

Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of AI and IoT technologies, deep learning for big data analytics, including affective learning, reinforcement learning, and transfer learning, are widely applied to sense, learn, and interact with human health. Examples of biomedical application include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, deep learning and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, deep learning, and big data analysis technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and deep learning technologies. Therefore, exploring the possibility of deep learning and big data analysis technology in the fields of biomedical applications and healthcare is in high demand.

This special issue invites a wide range of researchers, both from the computer science community and the biomedical research groups, to submit up-to-date results in cutting-edge deep learning and big data analysis technologies in biomedical and healthcare applications. With the emergence of novel methods and techniques in AI, machine learning, and deep learning, research results from both AI-based and traditional methods will be closely connected, bringing significant impacts on data mining, machine learning, computer vision, biomedical research, healthcare engineering, etc. Topics of interest include (but are not limited to):