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Artificial Intelligence in Radiation Therapy
This book constitutes the refereed proceedings of the First International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 20 full papers presented were carefully reviewed and selected from 24 submissions. The papers discuss the state of radiation therapy, the state of AI and related technologies, and hope to find a pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.
Machine Learning in Radiation Oncology
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Artificial Intelligence in Radiation Oncology and Biomedical Physics
This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics. AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and s...
Large Language Models
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration...
Principles and Advanced Methods in Medical Imaging and Image Analysis
Computerized medical imaging and image analysis have been the central focus in diagnostic radiology. They provide revolutionarizing tools for visualization of physiology as well as the understanding and quantitative measurement of physiological parameters. This book provides a unique depth of knowledge from the principles to recent advanced methods in medical imaging instrumentation and techniques as well as multidimensional image analysis and classification methods for research, education and applications in computer-aided diagnostic radiology. Internationally renowned researchers and experts in their respective areas provide detailed description of the basic foundation as well as the most recent developments in medical imaging. This book helps readers to understand theoretical and advanced concepts for important research and clinical applications.
Professional Office Business Application Development
Office Business Application (OBA) development is new and growing at a rapid pace as large companies discover its importance. This book provides you with insight into how organizations can leverage OBA. You’ll go on an in-depth journey to learning how to use various tools to bridge the gap between business data and the people who need it. Plus, you’ll gain an extensive understanding on building and deploying OBA via a common scenario and that is broken down so that you can learn each component.
Machine Learning in Medical Imaging
This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.
Handbook of Anatomical Models for Radiation Dosimetry
Over the past few decades, the radiological science community has developed and applied numerous models of the human body for radiation protection, diagnostic imaging, and nuclear medicine therapy. The Handbook of Anatomical Models for Radiation Dosimetry provides a comprehensive review of the development and application of these computational mode
