Description
Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images, 3D volumes, or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make auscultation reports in text format; electrocardiograms can be printed in time series format, x-rays saved as images; volume can be provided through angiography; temporal information by echocardiograms, and 4D information extracted through flow MRI. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance. These large and highly diverse amounts of information need to be organized and mined in an appropriate way so that meaningful information can be extracted. New multimodal data fusion techniques are able to combine salient information into one single source to ensure better diagnostic accuracy and assessment.Data Fusion Techniques and Applications for Smart Healthcare covers cutting-edge research from both academia and industry with a particular emphasis on recent advances in algorithms and applications that involve combining multiple sources of medical information. This book can be used as a reference for practicing engineers, scientists, and researchers. It will also be useful for graduate students and practitioners from government and industry as well as healthcare technology professionals working on state-of-the-art information fusion solutions for healthcare applications.- Presents broad coverage of applied case studies using data fusion techniques to mine, organize, and interpret medical data- Investigates how data fusion techniques offer a new solution for dealing with massive amounts of medical data coming from diverse sources and multiple formats- Focuses on identifying challenges, solutions, and new directions that will be useful for graduate students, researchers, and practitioners from government, academia, industry, and healthcare
Table of Contents
Editors' Preface to Data Fusion Techniques and Applications for Smart Healthcare1. Retinopathy Screening from OCT Imagery via Deep Learning2. Multi-sensor data fusion in digital twins for smart healthcare3. Deep Learning for Multi-source Medical Information Processing4. Robust watermarking algorithm based on multimodal medical image fusion5. Fusion based Robust and Secure Watermarking Method for e-Healthcare Applications6. Recent Advancements in Deep Learning-based Remote Photoplethysmography Methods7. Federated Learning in Healthcare Applications8. Riemannian Deep Feature Fusion with auto-encoders for MEG Depression Classification in Smart Healthcare applications9. Epileptic Spike Localization using MEG MRI modality Fusion for Intelligent Smart Healthcare10. Early classification of time series data: Overview, Challenges, and Opportunities11. Deep Learning based multimodal medical image fusion12. Data fusion in internet of medical things: Towards trust management, security and privacy13. Feature fusion for medical data14. Review on Hybrid Feature Selection and Classification of Microarray Gene Expression Data15. MFFWmark: Multi focused fusion based image watermarking for telemedicine applications with BRISK feature authentication16. Distributed Information Fusion for Secured Healthcare17. Deep Learning for Emotion Recognition using Physiological Signals
