Graph Neural Networks for Neurological Disorders: Fundamentals, Applications and Benefits in Research and Diagnostics
暫譯: 神經網絡在神經疾病中的應用：基礎、應用及其在研究與診斷中的益處
Hassan, MD Mehedi, Nag, Anindya, Islam, Shariful
- 出版商: Springer
- 出版日期: 2025-11-02
- 售價: $8,730
- 貴賓價: 9.5 折 $8,294
- 語言: 英文
- 頁數: 242
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 303204314X
- ISBN-13: 9783032043146
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相關分類:
DeepLearning
海外代購書籍(需單獨結帳)
相關主題
商品描述
This book represents a unique and comprehensive resource for understanding the intersection of advanced artificial intelligence (AI) and neurology. By focusing on graph neural networks (GNNs), the book addresses a crucial gap in the current literature, providing valuable insights into the analysis and interpretation of complex brain networks and neurological data. Intended for a diverse audience, including clinicians, scientists, researchers, and students, it demystifies the complexities of GNNs and their applications in neurology. For clinicians and healthcare practitioners, the book illustrates how GNNs can enhance diagnostic accuracy, inform personalized treatment plans and predict disease progression. This leads to improved patient outcomes and a deeper understanding of neurological conditions such as Alzheimer's, Parkinson's, multiple sclerosis and epilepsy. Researchers will find the book particularly valuable as it delves into the methodologies and technical aspects of GNNs, showcasing their ability to handle diverse data sources including genetic, imaging and clinical information. By integrating these datasets, GNNs reveal hidden patterns and biomarkers, offering new avenues for research and potential therapeutic targets.
A Guide to Graph Neural Networks for Neurological Disorders addresses the challenge of missing data, a common issue in neurological research, and demonstrates how GNNs can manage and mitigate these gaps. For students, both undergraduate and postgraduate, the book serves as an educational tool, providing clear explanations and practical examples that make complex concepts accessible. It equips the next generation of neuroscientists and data scientists with the knowledge and skills needed to contribute to this rapidly evolving field. The book aims to provide a foundational understanding of GNNs, demonstrate their practical applications in neurology, and inspire further research and innovation. By bridging the gap between AI and medical practice, the book empowers readers to leverage cutting-edge technology in the quest to understand and treat neurological illnesses, ultimately enhancing the quality of care and advancing the field of neuroscience.
商品描述(中文翻譯)
這本書是一個獨特且全面的資源，旨在理解先進人工智慧（AI）與神經學的交集。透過專注於圖神經網絡（GNNs），本書填補了當前文獻中的一個重要空白，提供了對複雜腦網絡和神經數據分析與解釋的寶貴見解。這本書的讀者群體多樣，包括臨床醫生、科學家、研究人員和學生，旨在揭開GNNs及其在神經學應用中的複雜性。對於臨床醫生和醫療從業人員，本書說明了GNNs如何提高診斷準確性、指導個性化治療計劃並預測疾病進展。這將改善病人的治療結果，並加深對阿茲海默症、帕金森病、多發性硬化症和癲癇等神經疾病的理解。研究人員會發現本書特別有價值，因為它深入探討了GNNs的方法論和技術層面，展示了它們處理包括基因、影像和臨床信息在內的多樣數據來源的能力。透過整合這些數據集，GNNs揭示了隱藏的模式和生物標記，為研究和潛在的治療目標提供了新的途徑。
《神經疾病的圖神經網絡指南》針對缺失數據的挑戰，這是神經研究中常見的問題，並展示了GNNs如何管理和減輕這些空白。對於本科生和研究生而言，本書作為一個教育工具，提供了清晰的解釋和實用的例子，使複雜的概念變得易於理解。它為下一代神經科學家和數據科學家提供了在這個快速發展的領域中所需的知識和技能。本書旨在提供GNNs的基礎理解，展示其在神經學中的實際應用，並激勵進一步的研究和創新。通過橋接AI與醫療實踐之間的鴻溝，本書使讀者能夠利用尖端技術來理解和治療神經疾病，最終提升護理質量並推進神經科學領域的發展。
作者簡介
Md Mehedi Hassan is a dedicated Ph.D. researcher in Computer and Information Science at the University of South Australia, where his work lies at the forefront of AI-powered medical imaging and healthcare informatics. With a strong foundation in Computer Science and Engineering (B.Sc., M.Sc.), he specializes in developing robust, clinically explainable AI systems for automated disease diagnosis, particularly focusing on liver diseases through advanced CT image analysis. His research intersects medical image processing, radiomics, deep learning, and clinical decision support systems, with an emphasis on real-time, scalable, and interpretable diagnostic models. Through innovative computational frameworks--including 3D segmentation networks, hybrid CNN architectures, radiomic feature extraction, and multimodal learning--he aims to transform how medical conditions are detected, staged, and monitored. Mehedi's work addresses critical gaps in healthcare by targeting challenges such as the automation of radiology workflows, early and precise disease classification, and reducing the diagnostic burden on clinicians. His projects are deeply aligned with translational medicine goals, ensuring that the tools he develops are not just academically rigorous but also clinically deployable. In addition to his research, he contributes to the scientific community as a journal editor, reviewer, and AI educator. He actively collaborates with clinicians, industry experts, and interdisciplinary teams to push the boundaries of healthcare AI, ensuring that each innovation contributes meaningfully to improved patient outcomes, health system efficiency, and global digital health equity.
Anindya Nag obtained an M.Sc. in Computer Science and Engineering from Khulna University in Khulna, Bangladesh, and a B.Tech. in Computer Science and Engineering from Adamas University in Kolkata, India. He is currently a lecturer in the Department of Computer Science and Engineering at the Northern University of Business and Technology in Khulna, Khulna 9100, Bangladesh. His research focuses on health informatics, medical Internet of Things, neuroscience, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored about 47 publications, including journal articles, conference papers, and book chapters, and has co-edited nine books.
Herat Joshi is a visionary leader in the field of Healthcare Informatics, healthcare technology and data management, currently serving as the Vice Chair at IEEE Iowa Illinois Section. Holding a Ph.D. in Computer Science & Engineering, Herat has a distinguished track record of pioneering healthcare solutions that integrate AI, IoT, and high-performance computing to enhance clinical outcomes. His notable achievements include leading EHR implementations across multiple health systems, advancing interoperability, and receiving prestigious accolades such as the Outstanding Leadership Award at the Health 2.0 Conference. A recognized thought leader, Herat is a Fellow of the American College of Health Data Management (FACDM), Vice Chair of an American Medical Informatics Association (AMIA) Workgroup, Senior Member of IEEE, Vice Chair of IEEE Iowa-Illinois Section and a Gartner Ambassador. He also contributes as a reviewer and editor for leading scientific journals.
Dr Shariful Islam (FESC, PhD, MPH, MBBS) an accomplished Associate Professor at Deakin University's Institute for Physical Activity and Nutrition, boasts an impressive academic and professional portfolio focused on Global Health and Digital Health. With a diverse educational background, including a Ph.D. in Medical Research, an MPH, and an MBBS, his expertise spans the design and execution of large-scale epidemiological studies, clinical trials, and implementation research. Dr. Islam's leadership in the Global Burden of Disease Australia project and membership in esteemed organizations like the WHO-ITU Working Group on Artificial Intelligence for Health underscore his commitment to leveraging innovative information technologies for the prevention and management of diabetes and cardiovascular disease. Through a plethora of research grants, projects, honors, and awards, he continues to make significant contributions to the field, shaping the landscape of healthcare intervention strategies and digital health innovations globally.
作者簡介(中文翻譯)
**Md Mehedi Hassan** 是南澳大利亞大學計算機與信息科學的專注博士研究員，他的工作位於人工智慧驅動的醫學影像和醫療信息學的最前沿。擁有計算機科學與工程的堅實基礎（學士、碩士），他專注於開發穩健且臨床可解釋的人工智慧系統，用於自動化疾病診斷，特別是通過先進的CT影像分析專注於肝臟疾病。他的研究交叉於醫學影像處理、放射組學、深度學習和臨床決策支持系統，強調實時、可擴展和可解釋的診斷模型。通過創新的計算框架——包括3D分割網絡、混合CNN架構、放射組學特徵提取和多模態學習——他旨在改變醫療狀況的檢測、分期和監測方式。Mehedi的工作針對醫療保健中的關鍵缺口，解決自動化放射學工作流程、早期和精確的疾病分類以及減輕臨床醫生的診斷負擔等挑戰。他的項目與轉化醫學目標深度對齊，確保他開發的工具不僅在學術上嚴謹，還能在臨床上部署。除了研究外，他還作為期刊編輯、審稿人和人工智慧教育者為科學社群做出貢獻。他積極與臨床醫生、行業專家和跨學科團隊合作，推動醫療保健人工智慧的邊界，確保每一項創新都能對改善病人結果、健康系統效率和全球數位健康公平做出有意義的貢獻。
**Anindya Nag** 獲得孟加拉國庫爾納大學的計算機科學與工程碩士學位，以及印度加爾各答的阿達馬斯大學的計算機科學與工程學士學位。他目前是孟加拉國庫爾納商業與科技大學計算機科學與工程系的講師。他的研究專注於健康信息學、醫療物聯網、神經科學和機器學習。他擔任多個知名期刊和國際會議的審稿人，並已發表和共同發表約47篇出版物，包括期刊文章、會議論文和書籍章節，並共同編輯了九本書籍。
**Herat Joshi** 是醫療信息學、醫療技術和數據管理領域的遠見領袖，目前擔任IEEE Iowa Illinois Section的副主席。擁有計算機科學與工程的博士學位，Herat在開創整合人工智慧、物聯網和高效能計算以提升臨床結果的醫療解決方案方面有著卓越的成就。他的顯著成就包括在多個健康系統中領導電子健康紀錄（EHR）實施、推進互操作性，並在Health 2.0會議上獲得傑出領導獎等榮譽。作為一位公認的思想領袖，Herat是美國健康數據管理學院（FACDM）的院士、美國醫療信息學協會（AMIA）工作組的副主席、IEEE的高級會員、IEEE Iowa-Illinois Section的副主席以及Gartner大使。他還作為領先科學期刊的審稿人和編輯做出貢獻。
**Dr Shariful Islam**（FESC, PhD, MPH, MBBS）是迪肯大學體育活動與營養研究所的傑出副教授，擁有專注於全球健康和數位健康的卓越學術和專業背景。擁有多元的教育背景，包括醫學研究的博士學位、公共衛生碩士學位和醫學士學位，他的專業知識涵蓋大規模流行病學研究、臨床試驗和實施研究的設計與執行。Dr. Islam在全球疾病負擔澳大利亞項目中的領導地位以及在世界衛生組織-國際電信聯盟人工智慧健康工作組的成員資格，突顯了他利用創新信息技術預防和管理糖尿病及心血管疾病的承諾。通過大量的研究資助、項目、榮譽和獎項，他持續對該領域做出重要貢獻，塑造全球醫療干預策略和數位健康創新的格局。
