Medical Imaging Using Deep Learning Intelligence Systems

Dear Colleagues,

Biomedical imaging techniques, deep learning, and artificial intelligence bring many healthcare protection benefits. We can collect, measure, and analyze vast volumes of health-related data using computing and networking technologies, leading to tremendous healthcare and excellent opportunities for medical imaging communities. Meanwhile, these technologies have also brought new challenges and issues. Predictive models, especially disease diagnosis and analysis, is considered one of the most promising directions for healthcare development.

Biomedical intelligent systems include hardware, computational models, databases, and software that optimize the acquisition, transmission, processing, storage, retrieval, analysis, and interpretation of vast volumes of multi-modal health-related data. Currently, these systems have been deployed in solutions that integrate a variety of technologies, including deep learning, computer vision, Internet of things, E-Health, bioinformatics, sensors, and so on, to achieve patient-centric healthcare. The aims of this Special Issue are 1) to present the state-of-the-art research on multi-modal computing for biomedical intelligence systems, and 2) to provide a forum for experts to disseminate their recent advances and views on future perspectives in the field. Researchers from academic fields and industries worldwide are encouraged to submit high quality unpublished original research articles and review articles in broad areas relevant to multi-modal computing theories and technologies for biomedical intelligence systems.

Dr. Mohamed Shehata
Dr. Agwad Eltantawy
Dr. Ramy Hussien
Dr. Ahmed Sherif
Guest Editors

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