Descripción
- Edition: 1
- Latest edition
- Published: October 1, 2026
- No. of pages (Paperback): 250
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443450105
Description
Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. The book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare.
Finally, the book addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.
Key features
- Explains key generative AI models (GANs, VAEs, Diffusion Models) tailored for neuroimaging data
- Presents case studies that showcase successful applications of generative AI in disease diagnosis and personalized medicine
- Discusses ethical considerations and best practices for responsible AI development in the context of neuroimaging
Table of contents
Part I: Foundations
1. Introduction to Neuroscience Imaging Modalities (fMRI, EEG, MEG, etc.) and their challenges.
2. A Primer on Generative AI: Key Concepts and Architectures
3. Data Handling and Preprocessing in Neuroimaging for Generative AI. Addressing noise, artifacts, and variability.
Part II: Methodological Advancements
4. Generative Adversarial Networks (GANs) For Neuroimaging Networks
5. Variational Autoencoders (VAEs) for Neuroimaging: Dimensionality Reduction, Data Augmentation, and Latent Space Analysis.
6. Diffusion Models for High-Fidelity Neuroimage Generation and Enhancement.
7. Flow-based Generative Models for Neuroimaging: Density Estimation and Data Augmentation.
8. Hybrid and Novel Generative Models for Neuroimaging: Exploring emerging architectures and combinations
Part III: Applications in Neuroscience
9. Generative AI for Disease Diagnosis and Prognosis (Alzheimer’s, Parkinson’s, Stroke, etc.)
10. Generative AI for Brain Connectivity and Network Analysis: Understanding brain organization and its alterations in disease
11. Generative AI for Simulating Brain Development and Aging: Modeling normal and pathological processes.
12. Generative AI for Cognitive Neuroscience: Investigating the neural basis of cognition.
Part IV: Challenges and Future Directions
13. Challenges and Limitations: Addressing data scarcity, interpretability, computational costs, and ethical concerns.
14. Generative Al for Neurological Diseases: A Review Toward Future Directions
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