Machine and Deep Learning in Oncology, Medical Physics and Radiology
暫譯: 腫瘤學、醫學物理學與放射學中的機器學習與深度學習
El Naqa, Issam, Murphy, Martin J.
- 出版商: Springer
- 出版日期: 2022-02-03
- 售價: $6,150
- 貴賓價: 9.5 折 $5,843
- 語言: 英文
- 頁數: 513
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3030830462
- ISBN-13: 9783030830465
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相關分類:
DeepLearning、物理學 Physics
海外代購書籍(需單獨結帳)
相關主題
商品描述
This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
商品描述(中文翻譯)
這本書現在已經進行了廣泛的修訂和更新,為第二版,提供了機器學習和深度學習的全面概述,以及它們在腫瘤學、醫學物理學和放射學中的角色。讀者將會發現這些領域的基本理論、方法和示範應用的詳細介紹。引言部分解釋了機器學習和深度學習,回顧了學習方法,討論了性能評估,並檢視了軟體工具和數據保護。隨後,書中詳細的各個部分專門討論了機器學習和深度學習在醫學影像分析、治療計劃和執行,以及結果建模和決策支持中的應用。每一章都提供了不同應用的資源,並根據需要嵌入了示範用的軟體代碼。這本書對於醫學物理學、放射學和腫瘤學的學生和住院醫師將是非常寶貴的資源,同時也會吸引更有經驗的從業者、研究人員以及應用機器學習社群的成員。
作者簡介
Issam El Naqa is founding Chair of Machine Learning Department and Associate Member of Radiation Oncology at Moffitt Cancer Center in Tampa, Florida.. He is board certified as a medical physicist by the American Board of Radiology. Dr. El Naqa received his BSc (1992) and MSc (1995) in Electrical and Communication Engineering from the University of Jordan, Jordan. He completed his PhD (2002) in Electrical and Computer Engineering at the Illinois Institute of Technology, Chicago, IL, USA, receiving the highest academic distinction award for his work. He also completed an MA (2007) in Biology Science at Washington University, where he was hired as an instructor (2005-7) and then an Assistant Professor (2007-10) in the departments of radiation oncology and the division of biomedical and biological sciences. He subsequently became an Associate Professor at McGill University Health Centre/Medical Physics Unit (2010-15). He later joined the Department of Radiation oncology at the University of Michigan at Ann Arbor (2015-20), where he was a Professor and associate member in Applied Physics and the Michigan institute of data science. Dr. El Naqa is a recognized authority in the fields of machine learning, data analytics, and oncology outcomes modeling and has published extensively in these areas with more than 200+ peer-reviewed journal publications and 4 edited textbooks. He has been a member and fellow of several academic and professional societies including AAPM and IEEE. His research has been funded by several federal and private grants in Canada and the USA and served on national and international study sections. He acts as a peer-reviewer and editorial board member for several leading international journals in his areas of expertise.
Martin J Murphy is Professor Emeritus of radiation oncology at Virginia Commonwealth University (VCU), where he directed research into image-guided surgery and radiation therapy, employing principles of machine learning and neural networks. He received his PhD in physics from the University of Chicago. Subsequently, he did research in nuclear physics, astrophysics, and space sciences at the Lawrence Berkeley Laboratory, the University of Washington, and the Lockheed Palo Alto Research Laboratory before joining the original development team for the CyberKnife in 1992. He continued CyberKnife development and other image-guidance applications at Stanford before joining the radiation oncology department at VCU in 2003. He has been the principal investigator for numerous NIH and private sector grants to apply robotics and machine learning to image guidance.
作者簡介(中文翻譯)
**Issam El Naqa** 是佛羅里達州坦帕市 Moffitt 癌症中心機器學習系的創始主席及放射腫瘤學的副成員。他是美國放射學委員會認證的醫學物理學家。El Naqa 博士於約旦大學獲得電機與通訊工程學士學位(1992年)及碩士學位(1995年)。他在美國伊利諾伊理工學院獲得電機與計算機工程博士學位(2002年),並因其研究獲得最高學術榮譽獎。他還於華盛頓大學獲得生物科學碩士學位(2007年),並在該校的放射腫瘤學系及生物醫學與生物科學部門擔任講師(2005-2007年)及助理教授(2007-2010年)。隨後,他成為麥吉爾大學健康中心/醫學物理單位的副教授(2010-2015年)。他於2015年至2020年在密西根大學安娜堡分校的放射腫瘤學系任教,擔任應用物理及密西根數據科學研究所的教授及副成員。El Naqa 博士在機器學習、數據分析及腫瘤結果建模領域享有盛譽,並在這些領域發表了超過200篇的同行評審期刊文章及4本編輯教科書。他是多個學術及專業社團的成員及研究員,包括 AAPM 和 IEEE。他的研究獲得了來自加拿大及美國的多項聯邦及私人資助,並參與國內外的研究小組。他擔任多本國際領先期刊的同行評審及編輯委員會成員。
**Martin J Murphy** 是維吉尼亞聯邦大學(VCU)放射腫瘤學的名譽教授,他曾指導有關影像引導手術及放射治療的研究,並運用機器學習及神經網絡的原則。他在芝加哥大學獲得物理學博士學位。隨後,他在洛倫斯伯克利國家實驗室、華盛頓大學及洛克希德帕洛阿爾托研究實驗室進行核物理、天體物理及太空科學的研究,並於1992年加入 CyberKnife 的原始開發團隊。他在斯坦福大學繼續進行 CyberKnife 的開發及其他影像引導應用,並於2003年加入 VCU 的放射腫瘤學系。他是多項 NIH 及私營部門資助的主要研究者,致力於將機器人技術及機器學習應用於影像引導。