Advances in Deep Generative Models for Healthcare and Medical Applications

1. Auflage 2025
ISBN: 978-1-032-98895-5
Verlag: Taylor & Francis Ltd

Buch, Englisch, 282 Seiten, Format (B × H): 156 mm x 234 mm

We have seen numerous ground-breaking achievements in generative AI, particularly in the areas of computer vision, voice processing, and natural language processing, in recent times. Synthetic human faces, artworks, and cohesive essays on many subjects can be produced with excellent quality by generative adversarial networks and diffusion models. Because of their ability to learn complicated features from healthcare data and medical imaging, generative models are also revolutionizing health care domain. Thus, Generative AI are helping with healthcare and computer-aided diagnostics. The recent success of deep generative models in areas such as text-to-image conversion, diffusion modeling, and large language modeling has brought them immense attention. Learning interpretable representations and integrating different modalities or previous information from domain knowledge are common learning goals for early well-established approaches like variational autoencoders, generative adversarial networks, and normalizing flows. New developments in this area have the potential to open up enormous opportunities in the healthcare field.

This book provides a platform for research on deep generative models, with an emphasis on its healthcare applications. The book addresses the unanswered questions that stop these approaches from making a huge difference in real-world clinical practice. The goal of this book is to bring together a wide range of methodologies which are using generative models in health care-related contexts. The book leverages the recent methodological advancements in deep generative models to address critical health-care challenges across all data-types, paving the way for their practical integration into the healthcare system and elevate their impact on the future of healthcare.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

S, Balasubramaniam

Kadry, Seifedine

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

Preface. Introduction To Deep Generative Models. A Deep Dive into Generative Model Types beyond Imagination. A New Wave of Generative Flow Networks in Streaming Healthcare with Artificial Intelligence. Diffusion Models for Medical Applications. Modeling Disease Progression with Generative Time Series Models: A New Approach to Complex Disease Trajectories. Advancing Synthetic EEG Signal Generation for Biomedical Research: A Diffusion Model Approach. Leveraging Semi-Supervised Diffusion Models for Accurate Brain Age Prediction. The Precision Edge of Deep Generative Models for Enhanced Differential Diagnosis. AI-Driven Implantable Medical Devices Using Deep Generative Models. Deep Generative AI-Based Multimodal Biometric Authentication System for enhanced Security and Accessibility in Healthcare Applications. Deep Generative Models for Alzheimer’s Disease Diagnosis. Regulating Deep Generative Models: Ethical and Legal Considerations for Healthcare AI. Research Challenges in Deep Generative Models for Healthcare and Medical Applications.

Über Autor(innen)

Dr. Balasubramaniam S (IEEE Senior Member) is an Assistant Professor in Kerala University of Digital Sciences, Innovation and Technology (Formerly IIITM-K), Digital University Kerala, Thiruvananthapuram, India. Before joining Digital University Kerala, he was a Senior Associate Professor at the School of Computer Science and Engineering at the Vellore Institute of Technology (VIT), Chennai, India. He has around 15+ years of experience in teaching, research and industry. He has completed his postdoctoral research in the Department  of Applied Data Science, Noroff University College, Kristiansand, Norway. He has a PhD in Computer Science and Engineering from Anna University, Chennai, India in 2015 and has published 25+ research papers in reputed SCI/WoS/Scopus indexed journals. He has also been granted 1 Australian patent and 2 Indian Patents and published 2 Indian patents. He has presented papers at conferences, contributed to and edited books published by global publishers. His research and publication interests include machine learning, deep and federated learning-based disease diagnosis, cloud computing security, Generative AI, and electric vehicles.

Prof. Seifedine Kadry has a bachelor’s degree in 1999 from Lebanese University, MS degree in 2002 from Reims University, France and EPFL, Lausanne, Switzerland, PhD in 2007 from Blaise Pascal University, France, HDR degree in 2017 from Rouen University, France. At present his research focuses on data science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a full professor of data science at Noroff University College, Norway and Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.