Generative Machine Learning Models in Medical Image Computing
暫譯: 醫學影像計算中的生成式機器學習模型
Zhang, Le, Chen, Chen, Li, Zeju
- 出版商: Springer
- 出版日期: 2025-03-13
- 售價: $6,780
- 貴賓價: 9.5 折 $6,441
- 語言: 英文
- 頁數: 382
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031809645
- ISBN-13: 9783031809644
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相關分類:
Machine Learning
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相關主題
商品描述
Generative Machine Learning Models in Medical Image Computing" provides a comprehensive exploration of generative modeling techniques tailored to the unique demands of medical imaging. This book presents an in-depth overview of cutting-edge generative models such as GANs, VAEs, and diffusion models, examining how they enable groundbreaking applications in medical image synthesis, reconstruction, and enhancement. Covering diverse imaging modalities like MRI, CT, and ultrasound, it illustrates how these models facilitate improvements in image quality, support data augmentation for scarce datasets, and create new avenues for predictive diagnostics.
Beyond technical details, the book addresses critical challenges in deploying generative models for healthcare, including ethical concerns, interpretability, and clinical validation. With a strong focus on real-world applications, it includes case studies and implementation guidelines, guiding readers in translating theory into practice. By addressing model robustness, reproducibility, and clinical utility, this book is an essential resource for researchers, clinicians, and data scientists seeking to leverage generative models to enhance biomedical imaging and deliver impactful healthcare solutions. Combining technical rigor with practical insights, it offers a roadmap for integrating advanced generative approaches in the field of medical image computing.
商品描述(中文翻譯)
《醫學影像計算中的生成機器學習模型》提供了針對醫學影像獨特需求的生成建模技術的全面探索。本書深入概述了尖端的生成模型，如生成對抗網絡（GANs）、變分自編碼器（VAEs）和擴散模型，並探討它們如何促進醫學影像合成、重建和增強的突破性應用。涵蓋了多種影像模式，如磁共振成像（MRI）、電腦斷層掃描（CT）和超聲波，說明這些模型如何改善影像質量、支持稀缺數據集的數據增強，並為預測診斷創造新的途徑。
除了技術細節外，本書還探討了在醫療保健中部署生成模型的關鍵挑戰，包括倫理問題、可解釋性和臨床驗證。強調實際應用的同時，書中包含案例研究和實施指南，指導讀者將理論轉化為實踐。通過解決模型的穩健性、可重複性和臨床實用性，本書是研究人員、臨床醫生和數據科學家尋求利用生成模型來增強生物醫學影像和提供有影響力的醫療解決方案的重要資源。結合技術嚴謹性和實用見解，本書為在醫學影像計算領域整合先進的生成方法提供了一條路線圖。
作者簡介
Dr. Le Zhang is an Assistant Professor at the School of Engineering, College of Engineering and Physical Sciences in the University of Birmingham. He was a Postdoc Researcher at the University of Oxford since 2022. Before that, he was a Research Fellow at University College London since 2019 working with Prof. Daniel Alexander. Under the supervision of Prof. Alejandro F Frangi, he obtained his Ph.D. in Medical Image Computing from the University of Sheffield in 2019.
Dr. Chen Chen is a Lecturer in Computer Vision, at the Department of Computer Science, University of Sheffield, a core member of Insigeno Institute and Shef.AI community. Previously, she was a post-doc at Oxford BioMedIA group, University of Oxford, and the Computing Department at Imperial College London (ICL). She was also a research scientist at HeartFlow. In 2022, she obtained her Ph.D. from the Department of Computing at Imperial College London, working closely with Prof. Daniel Rueckert and Dr. Wenjia Bai.
Dr. Zeju Li is currently a Post-Doctoral Researcher in FMRIB Analysis Group, University of Oxford, working with Prof. Saad Jbabdi. Previously, he obtained his PhD in Computing from BioMedIA Group with Prof. Ben Glocker, Imperial College London. During his PhD, he spent time in MIRACLE Group (Institute of Computing Technology) and Huawei Noah's Ark Lab (London). He got both his MSc and BSc from the Department of Electronic Engineering, Fudan University.
Greg Slabaugh is Professor of Computer Vision and AI and Director of the Digital Environment Research Institute (DERI) at Queen Mary University of London. He is also Turing Liaison (Academic) for Queen Mary at The Alan Turing Institute. He earned a PhD in Electrical Engineering from Georgia Institute of Technology in Atlanta, USA. Previously, he was Chief Scientist in Computer Vision (EU) for Huawei Technologies R&D, and other prior appointments include City, University of London, Medicsight, and Siemens. He holds 38 granted patents and has approximately 200 per-reviewed publications. He regularly serves on the technical program committe for computer vision and machine learning conferences (CVPR, NeurIPS, AAAI) and related journals.
作者簡介(中文翻譯)
張樂博士是伯明翰大學工程學院工程與物理科學學院的助理教授。他自2022年起在牛津大學擔任博士後研究員。在此之前，他自2019年起在倫敦大學學院擔任研究員，與丹尼爾·亞歷山大教授合作。在亞歷杭德羅·F·弗朗基教授的指導下，他於2019年在謝菲爾德大學獲得醫學影像計算的博士學位。
陳晨博士是謝菲爾德大學計算機科學系的計算機視覺講師，也是Insigeno Institute和Shef.AI社群的核心成員。之前，她在牛津大學的Oxford BioMedIA小組和倫敦帝國學院的計算系擔任博士後研究員。她還曾在HeartFlow擔任研究科學家。她於2022年在倫敦帝國學院的計算系獲得博士學位，並與丹尼爾·魯克特教授和白文佳博士密切合作。
李澤舉博士目前是牛津大學FMRIB分析小組的博士後研究員，與薩德·賈巴迪教授合作。之前，他在倫敦帝國學院的BioMedIA小組獲得計算博士學位，指導教授為本·格洛克教授。在攻讀博士學位期間，他曾在MIRACLE小組（計算技術研究所）和華為的諾亞方舟實驗室（倫敦）工作。他的碩士和學士學位均來自復旦大學電子工程系。
格雷格·斯拉博博士是倫敦女王瑪麗大學計算機視覺與人工智慧的教授，以及數位環境研究所（DERI）的主任。他同時也是艾倫·圖靈研究所的女王瑪麗大學學術聯絡人。他在美國喬治亞理工學院獲得電氣工程博士學位。之前，他曾擔任華為技術有限公司計算機視覺（歐洲）首席科學家，並曾在倫敦城市大學、Medicsight和西門子等機構任職。他擁有38項已授權專利，並發表了約200篇經過同行評審的論文。他定期擔任計算機視覺和機器學習會議（CVPR、NeurIPS、AAAI）及相關期刊的技術程序委員會成員。
