Data Fusion Techniques and Applications for Smart Healthcare
- 1st Edition - March 12, 2024
- Imprint: Academic Press
- Editors: Amit Kumar Singh, Stefano Berretti
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 2 3 3 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 2 3 4 - 6
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 algori… Read more
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Request a sales quoteData 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. The book can be used as a reference for practicing engineers, scientists, and researchers, but 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.
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
Researchers, scientists, developers, practitioners, and graduate students from academia and industry working in the major and interdisciplinary areas of computer science/engineering and biomedical engineering, specifically on data processing and security solutions for smart healthcare and medical data systems
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
- Edition: 1
- Published: March 12, 2024
- No. of pages (Paperback): 442
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443132339
- eBook ISBN: 9780443132346
AS
Amit Kumar Singh
Amit Kumar Singh is an associate professor at the Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India. Dr. Singh have been recognized as "World Ranking of Top 2% Scientists" in the area of “Biomedical Research" (for Year 2019) and "Artificial Intelligence & Image Processing" (for the Year 2020 and 2021) according to the survey given by Stanford University, USA. Currently, Dr. Singh is the Associate Editor of IEEE Trans. on Multimedia, ACM Trans. Multimedia Comput. Commun. Appl., IEEE Trans. Computat. Social Syst., IEEE Trans. Ind. Informat., IEEE J. Biomed. Heal. Informatics Etc. His research interests include multimedia data hiding, image processing, compression, biometrics, Cryptography.
Affiliations and expertise
Associate Professor, Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, IndiaSB
Stefano Berretti
Prof. Berretti is an associate professor at the Media Integration and Communication Center (MICC) and Department of Information Engineering (DINFO) of the University of Florence (UNIFI), Florence, Italy. In 2017, he obtained habilitation as full professor in Computer Engineering. Prof. Berretti has worked on image databases for effective and efficient image retrieval based on color, shape attributes and spatial relationships. He also investigated the problem of retrieval from repositories distributed on the net using resource selection and results fusion. More recently, his research interests focused on deep learning methods for face recognition, and to their generalization to non-Euclidean domains (i.e., graphs, meshes, manifolds, etc.). He is information director and associate editor of the ACM Transactions on Multimedia Computing, Communication and Applications, and of the IET Computer Vision journal. He is a member of the ACM, and senior member of the IEEE.
Affiliations and expertise
Associate Professor, Department of Information Engineering, University of Florence(UNIFI), Florence, Italy.Read Data Fusion Techniques and Applications for Smart Healthcare on ScienceDirect