Data Fusion Techniques and Applications for Smart Healthcare: Intelligent Data-Centric Systems
Editat de Amit Kumar Singh, Stefano Berrettien Limba Engleză Paperback – 18 mar 2024
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.
- 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
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Specificații
ISBN-13: 9780443132339
ISBN-10: 044313233X
Pagini: 442
Dimensiuni: 191 x 235 x 26 mm
Greutate: 0.92 kg
Editura: ELSEVIER SCIENCE
Seria Intelligent Data-Centric Systems
ISBN-10: 044313233X
Pagini: 442
Dimensiuni: 191 x 235 x 26 mm
Greutate: 0.92 kg
Editura: ELSEVIER SCIENCE
Seria Intelligent Data-Centric Systems
Cuprins
Editors' Preface to Data Fusion Techniques and Applications for Smart Healthcare
1. Retinopathy Screening from OCT Imagery via Deep Learning
2. Multi-sensor data fusion in digital twins for smart healthcare
3. Deep Learning for Multi-source Medical Information Processing
4. Robust watermarking algorithm based on multimodal medical image fusion
5. Fusion based Robust and Secure Watermarking Method for e-Healthcare Applications
6. Recent Advancements in Deep Learning-based Remote Photoplethysmography Methods
7. Federated Learning in Healthcare Applications
8. Riemannian Deep Feature Fusion with auto-encoders for MEG Depression Classification in Smart Healthcare applications
9. Epileptic Spike Localization using MEG MRI modality Fusion for Intelligent Smart Healthcare
10. Early classification of time series data: Overview, Challenges, and Opportunities
11. Deep Learning based multimodal medical image fusion
12. Data fusion in internet of medical things: Towards trust management, security and privacy
13. Feature fusion for medical data
14. Review on Hybrid Feature Selection and Classification of Microarray Gene Expression Data
15. MFFWmark: Multi focused fusion based image watermarking for telemedicine applications with BRISK feature authentication
16. Distributed Information Fusion for Secured Healthcare
17. Deep Learning for Emotion Recognition using Physiological Signals
1. Retinopathy Screening from OCT Imagery via Deep Learning
2. Multi-sensor data fusion in digital twins for smart healthcare
3. Deep Learning for Multi-source Medical Information Processing
4. Robust watermarking algorithm based on multimodal medical image fusion
5. Fusion based Robust and Secure Watermarking Method for e-Healthcare Applications
6. Recent Advancements in Deep Learning-based Remote Photoplethysmography Methods
7. Federated Learning in Healthcare Applications
8. Riemannian Deep Feature Fusion with auto-encoders for MEG Depression Classification in Smart Healthcare applications
9. Epileptic Spike Localization using MEG MRI modality Fusion for Intelligent Smart Healthcare
10. Early classification of time series data: Overview, Challenges, and Opportunities
11. Deep Learning based multimodal medical image fusion
12. Data fusion in internet of medical things: Towards trust management, security and privacy
13. Feature fusion for medical data
14. Review on Hybrid Feature Selection and Classification of Microarray Gene Expression Data
15. MFFWmark: Multi focused fusion based image watermarking for telemedicine applications with BRISK feature authentication
16. Distributed Information Fusion for Secured Healthcare
17. Deep Learning for Emotion Recognition using Physiological Signals