Generative Artificial Intelligence for Neuroimaging : Methods and Applications

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予約

Generative Artificial Intelligence for Neuroimaging : Methods and Applications

Koundal, Deepika (EDT)/ Hemanth, D. Jude (EDT)

ウェブストア価格 ¥41,184（本体¥37,440）
Academic Press Inc（2026/10発売）
外貨定価 US$ 180.00
ポイント 374pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 250 p.
言語 ENG
商品コード 9780443450105

Full 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.

Part I: Foundations
1. Introduction to Neuroscience Imaging Modalities (fMRI, EEG, MEG, etc.) and Their Challenges
2. A Primer on Generative Artificial Intelligence: Key Concepts and Architectures (GANs, VAEs, Diffusion Models, Flow-based Models, etc.). Emphasis on Explainability and Interpretability
3. Data Handling and Preprocessing in Neuroimaging for Generative AI. Dealing with Noise, Artifacts, and Variability

Part II: Methodological Advancements
4. Generative Adversarial Networks (GANs) for Neuroimaging: Applications and Limitations
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 Personalized Medicine in Neuroscience: Tailoring Treatments and Interventions
11. Generative AI for Brain Connectivity and Network Analysis: Understanding Brain Organization and Its Alterations in Disease
12. Generative AI for Simulating Brain Development and Aging: Modeling Normal and Pathological Processes
13. Generative AI for Cognitive Neuroscience: Investigating the Neural Basis of Cognition

Part IV: Challenges and Future Directions
14. Challenges and Limitations: Addressing Data Scarcity, Interpretability, Computational Costs, and Ethical Concerns
15. Future Directions and Research Opportunities: Identifying Promising Areas for Future Development and Innovation