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Data Fusion Techniques and Applications for Smart Healthcare
1st Edition - March 1, 2024
Editors: Amit Kumar Singh, Stefano Berretti
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 3 2 3 3 - 9
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… Read more
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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
- Chapters focus on identifying challenges, solutions and new directions which 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
1. Introduction to Data Fusion Techniques and Applications for Healthcare Data (Editors)
2. Retinopathy analysis and prognosis: A fusion framework
3. Multi-sensor data fusion in digital twins for smart healthcare
4. Regression based multi-modal medical image fusion
5. Data tokenization and linkage for electronic medical records
6. Data fusion in Internet of Medical Things
7. Feature fusion for medical data
8. Early classification on time series data: Overview, Challenges, and Opportunities
9. Adversarial attacks on textual data
10. Explainability in leukemia recognition with symbolic learning
11. Data fusion in internet of medical things: Towards trust management, security and privacy
12. Secure watermarking in fused medical image for smart healthcare
13. A Smart Surgery Guide (SSG): An innovative deep learning-based surgery approach for Healthcare Informatics
14. Distributed Information Fusion for Secured Healthcare
15. Advancements in Data Fusion and Watermarking of Medical Images: A Critical Analysis
16. A review of deep learning based medical image fusion techniques
17. Advanced analysis of soft computing techniques for prediction of chronic diseases
18. Review on Hybrid Feature Selection and Classification of Microarray Gene Expression Data
2. Retinopathy analysis and prognosis: A fusion framework
3. Multi-sensor data fusion in digital twins for smart healthcare
4. Regression based multi-modal medical image fusion
5. Data tokenization and linkage for electronic medical records
6. Data fusion in Internet of Medical Things
7. Feature fusion for medical data
8. Early classification on time series data: Overview, Challenges, and Opportunities
9. Adversarial attacks on textual data
10. Explainability in leukemia recognition with symbolic learning
11. Data fusion in internet of medical things: Towards trust management, security and privacy
12. Secure watermarking in fused medical image for smart healthcare
13. A Smart Surgery Guide (SSG): An innovative deep learning-based surgery approach for Healthcare Informatics
14. Distributed Information Fusion for Secured Healthcare
15. Advancements in Data Fusion and Watermarking of Medical Images: A Critical Analysis
16. A review of deep learning based medical image fusion techniques
17. Advanced analysis of soft computing techniques for prediction of chronic diseases
18. Review on Hybrid Feature Selection and Classification of Microarray Gene Expression Data
- No. of pages: 300
- Language: English
- Published: March 1, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443132339
AS
Amit Kumar Singh
Dr. Amit Kumar Singh is an assistant professor at the Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India. He has authored more than 100 peer-reviewed journal, conference publications and book chapters, three books and edited five books with internationally recognized publishers such Springer and Elsevier. He has edited various international journal special issues as a lead guest editor such as IEEE Transactions on Industrial Informatics, ACM Transactions on Multimedia Computing, Communications, and Applications, ACM Transactions on Internet Technology, IEEE Access, Multimedia Tools and Applications, Springer, International Journal of Information Management, Elsevier, Journal of Ambient Intelligence and Humanized Computing, Springer Etc. He has obtained memberships from several international academic organizations such as ACM and IEEE. His research interests include multimedia data hiding, image processing, biometrics, & Cryptography.
Affiliations and expertise
Assistant 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.