Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support
Will be published approx. on 10. January 2027
Book
Paperback/Softback
440 pages
978-0-443-51640-5 (ISBN)
Description
Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support examines how to fuse imaging, genomics, electronic health records, and wearable sensor data into clinically actionable insights. As healthcare data becomes increasingly diverse and voluminous, there is a pressing need for integrative methodologies that preserve information across modalities while maintaining interpretability and safety. Current resources either focus on single-modality AI or domain-specific applications, leaving practitioners with fragmented guidance. This volume defines a cohesive, data-centric framework for multimodal predictive diagnostics and clinical decision support, addressing methodological foundations, reproducible pipelines, and real-world translation challenges.
More details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-0-443-51640-5 (9780443516405)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Dr. Manoj Diwakar is currently working as Associate professor in the Department of Computer Science and Engineering at Graphic Era Deemed to be University, Dehradun. With more than a decade of industrial and academic experience, he is committed and dedicated to the continuous upliftment of the research environment in the department. His research interests include Image Processing, Information Security and Medical Imaging. He has published more than 110 research papers in peer-reviewed journals, conferences, books and book chapters with national and international publishers of repute. He has also served as Guest editors of many reputed journals. He organized many international conferences. He has served as Associate editors/Editorial members of many reputed journals .
Dr. Prabhishek Singh is working (Senior IEEE Member) as an Assistant Professor in School of Computer Science Engineering and Technology, Bennett University (Times of India Group), Greater Noida, India since 2022. He has total teaching and research experience of 8 years. He did his Ph.D. in 2018. He did his M. Tech in 2013, and B.Tech in 2010. He is also awarded with young scientist award and excellent researcher award. He has published 100+ research papers in SCI/SCIE/Scopus, ESCI journals, and conferences. His research interest includes Image Processing and Computer Vision, Deep Learning, and Machine Learning. He is serving as an Associate Editor, Academic Editor, Review Editor, Guest Editor, Reviewer, and Editorial Committee Chair of many SCI/SCIE/Scopus and ESCI journals, and other prestigious conferences.
Sweta Sneha works in the Michael J. Coles College of Business at Kennesaw State University, USA. Dr Akbar Sheikh-Akbari is a Reader (Associate Professor) in the School of Built Environment, Engineering and Computing at Leeds Beckett University. He holds a PhD in Electronic and Electrical Engineering from the University of Strathclyde. His research focuses on biometric identification, hyperspectral imaging, colour processing, image super-resolution, multiview image/video systems, deep learning, and artificial intelligence. Dr. Sheikh-Akbari has published over 100 peer-reviewed papers and has led several funded projects, including recent work on hyperspectral imaging for aflatoxin detection and RFID-based asset management. He has also supervised ten PhD and two MRes researchers to successful completion.
Dr. Prabhishek Singh is working (Senior IEEE Member) as an Assistant Professor in School of Computer Science Engineering and Technology, Bennett University (Times of India Group), Greater Noida, India since 2022. He has total teaching and research experience of 8 years. He did his Ph.D. in 2018. He did his M. Tech in 2013, and B.Tech in 2010. He is also awarded with young scientist award and excellent researcher award. He has published 100+ research papers in SCI/SCIE/Scopus, ESCI journals, and conferences. His research interest includes Image Processing and Computer Vision, Deep Learning, and Machine Learning. He is serving as an Associate Editor, Academic Editor, Review Editor, Guest Editor, Reviewer, and Editorial Committee Chair of many SCI/SCIE/Scopus and ESCI journals, and other prestigious conferences.
Sweta Sneha works in the Michael J. Coles College of Business at Kennesaw State University, USA. Dr Akbar Sheikh-Akbari is a Reader (Associate Professor) in the School of Built Environment, Engineering and Computing at Leeds Beckett University. He holds a PhD in Electronic and Electrical Engineering from the University of Strathclyde. His research focuses on biometric identification, hyperspectral imaging, colour processing, image super-resolution, multiview image/video systems, deep learning, and artificial intelligence. Dr. Sheikh-Akbari has published over 100 peer-reviewed papers and has led several funded projects, including recent work on hyperspectral imaging for aflatoxin detection and RFID-based asset management. He has also supervised ten PhD and two MRes researchers to successful completion.
Editor
Associate Professor, Department of Computer Science and Engineering, Graphic Era Deemed to be University, India
Assistant Professor, School of Computer Science Engineering and Technology, Bennett University, India
Michael J. Coles College of Business, Kennesaw State University, USA
Associate Professor, School of Built Environment, Engineering and Computing, Leeds Beckett University, UK
Content
1. Introduction to Multimodal Healthcare Data
2. Deep Learning Architectures for Multimodal Fusion
3. Explainability and Trustworthiness in Predictive AI
4. Data Curation, Preprocessing, and Harmonization
5. Evaluation Metrics and Clinical Benchmarks
6. Deep Learning for Medical Image Classification and Segmentation
7. Applications of CNNs, U-Nets, and transformers in disease detection.
8. Multimodal AI for Early Cancer Detection
9. Cardiovascular Disease Prediction Using Imaging and EHR Data
10. Neurological Disease Prediction from Brain Imaging
11. Respiratory and Pulmonary Imaging for Disease Forecasting
12. Metabolic and Endocrine Disorders: Imaging-Aided Prediction
13. Infectious Disease and Public Health Surveillance with Imaging and Clinical Data
14. Rare and Pediatric Diseases: Challenges in Imaging-Based Prediction
15. AI-Driven Decision Support in Radiology and Pathology
16. Integrating Wearable, IoT, and Imaging Data for Personalized Medicine
17. Generative AI for Medical Image Synthesis and Simulation using GANs, diffusion models, and digital twins for predictive healthcare.
18. Open Challenges, Limitations, and Future Roadmap
2. Deep Learning Architectures for Multimodal Fusion
3. Explainability and Trustworthiness in Predictive AI
4. Data Curation, Preprocessing, and Harmonization
5. Evaluation Metrics and Clinical Benchmarks
6. Deep Learning for Medical Image Classification and Segmentation
7. Applications of CNNs, U-Nets, and transformers in disease detection.
8. Multimodal AI for Early Cancer Detection
9. Cardiovascular Disease Prediction Using Imaging and EHR Data
10. Neurological Disease Prediction from Brain Imaging
11. Respiratory and Pulmonary Imaging for Disease Forecasting
12. Metabolic and Endocrine Disorders: Imaging-Aided Prediction
13. Infectious Disease and Public Health Surveillance with Imaging and Clinical Data
14. Rare and Pediatric Diseases: Challenges in Imaging-Based Prediction
15. AI-Driven Decision Support in Radiology and Pathology
16. Integrating Wearable, IoT, and Imaging Data for Personalized Medicine
17. Generative AI for Medical Image Synthesis and Simulation using GANs, diffusion models, and digital twins for predictive healthcare.
18. Open Challenges, Limitations, and Future Roadmap