Special Issue on
Deep Learning for Multimedia Healthcare

Springer Multimedia Systems Journal

Scope
Digital health generates a huge amount of multimedia healthcare data in the form of text, radiological images, audio, video and so forth. Since the start of the COVID-19 pandemic, we have witnessed an incremental increase in the present healthcare data. Such large-scale multimedia healthcare data creates challenges and opportunities for multimedia healthcare data analysis. AI, and more specifically as deep learning (DL) algorithms, have been widely used by researchers for handling the massive volume of epidemic data, predicting the live epidemic crisis and initiating new research directions to analyze healthcare multimedia data. Therefore, deep learning for multimedia healthcare data analysis is becoming an emerging research area in the field of multimedia and computer vision.

This special issue is intended to report high-quality research on recent advances in Deep Learning for multimedia healthcare, specifically state-of-the-art approaches, methodologies, and systems for the design, development, deployment, and innovative use of those convergent technologies for providing insights into multimedia healthcare service demands. Authors are solicited to submit unpublished papers in the following topics. Topic include but are not restricted to:

• DL-based multimedia healthcare data analysis
• Multimedia healthcare data fusion for speedy detection and diagnosis for infectious diseases
• DL-based patient monitoring and predicting the spread of infectious diseases
• DL-based techniques, algorithms, and methods to monitor and track casualties and contacts of epidemic diseases
• DL-based multimedia big data analysis for tracking infections, and health monitoring
• DL-based detection of COVID-19 patients
• Advanced DL-based medical image analysis techniques for long-term and short-term risk prediction of infectious diseases
• DL-driven infected patient monitoring though the analysis of chest CT and RT-PCR
• DL for Lung and infection segmentation for epidemic diseases
• Data collections, benchmarking, and performance evaluation for DL-driven multimedia healthcare