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.
1. Data Fusion Techniques and Applications for Smart Healthcare - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. List of contributors - p. Page v 8. Preface - p. Page ix 9. List of Illustrations - p. Page xx 10. List of Tables - p. Page xxi 11. Chapter 1 : Retinopathy screening from OCT imagery via deep learning - p. Page xxi 12. Chapter 2 : Multisensor data fusion in Digital Twins for smart healthcare - p. Page 16 13. Chapter 3 : Deep learning for multisource medical information processing - p. Page 42 14. Chapter 4 : Robust watermarking algorithm based on multimodal medical image fusion - p. Page 70 15. Chapter 5 : Fusion-based robust and secure watermarking method for e-healthcare applications - p. Page 105 16. Chapter 6 : Recent advancements in deep learning-based remote photoplethysmography methods - p. Page 124 17. Chapter 7 : Federated learning in healthcare applications - p. Page 149 18. Chapter 8 : Riemannian deep feature fusion with autoencoder for MEG depression classification in smart healthcare applications - p. Page 188 19. Chapter 9 : Source localization of epileptiform MEG activity towards intelligent smart healthcare: a retrospective study - p. Page 211 20. Chapter 10 : Early classification of time series data: overview, challenges, and opportunities - p. Page 223 21. Chapter 11 : Deep learning-based multimodal medical image fusion - p. Page 246 22. Chapter 12 : Data fusion in Internet of Medical Things: towards trust management, security, and privacy - p. Page 274 23. Chapter 13 : Feature fusion for medical data - p. Page 295 24. Chapter 14 : Review on hybrid feature selection and classification of microarray gene expression data - p. Page 314 25. Chapter 15 : MFFWmark: multifocus fusion-based image watermarking for telemedicine applications with BRISK feature authentication - p. Page 338 26. Chapter 16 : Distributed information fusion for secure healthcare - p. Page 358 27. Chapter 17 : Deep learning for emotion recognition using physiological signals - p. Page 381 28. Index - p. Page 399
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.
1. Data Fusion Techniques and Applications for Smart Healthcare - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. List of contributors - p. Page v 8. Preface - p. Page ix 9. List of Illustrations - p. Page xx 10. List of Tables - p. Page xxi 11. Chapter 1 : Retinopathy screening from OCT imagery via deep learning - p. Page xxi 12. Chapter 2 : Multisensor data fusion in Digital Twins for smart healthcare - p. Page 16 13. Chapter 3 : Deep learning for multisource medical information processing - p. Page 42 14. Chapter 4 : Robust watermarking algorithm based on multimodal medical image fusion - p. Page 70 15. Chapter 5 : Fusion-based robust and secure watermarking method for e-healthcare applications - p. Page 105 16. Chapter 6 : Recent advancements in deep learning-based remote photoplethysmography methods - p. Page 124 17. Chapter 7 : Federated learning in healthcare applications - p. Page 149 18. Chapter 8 : Riemannian deep feature fusion with autoencoder for MEG depression classification in smart healthcare applications - p. Page 188 19. Chapter 9 : Source localization of epileptiform MEG activity towards intelligent smart healthcare: a retrospective study - p. Page 211 20. Chapter 10 : Early classification of time series data: overview, challenges, and opportunities - p. Page 223 21. Chapter 11 : Deep learning-based multimodal medical image fusion - p. Page 246 22. Chapter 12 : Data fusion in Internet of Medical Things: towards trust management, security, and privacy - p. Page 274 23. Chapter 13 : Feature fusion for medical data - p. Page 295 24. Chapter 14 : Review on hybrid feature selection and classification of microarray gene expression data - p. Page 314 25. Chapter 15 : MFFWmark: multifocus fusion-based image watermarking for telemedicine applications with BRISK feature authentication - p. Page 338 26. Chapter 16 : Distributed information fusion for secure healthcare - p. Page 358 27. Chapter 17 : Deep learning for emotion recognition using physiological signals - p. Page 381 28. Index - p. Page 399
