Download or read book Brainlesion Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries written by Alessandro Crimi and published by Springer. This book was released on 2018-02-16 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.

Download or read book Deep Learning and Data Labeling for Medical Applications written by Gustavo Carneiro and published by Springer. This book was released on 2016-10-07 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

Download or read book Medical Image Analysis written by Alejandro Frangi and published by Academic Press. This book was released on 2023-09-20 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing

Download or read book Head and Neck Tumor Segmentation written by Vincent Andrearczyk and published by Springer Nature. This book was released on 2021-01-12 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results.

Download or read book Advanced Computational Intelligence Methods for Processing Brain Imaging Data written by Kaijian Xia and published by Frontiers Media SA. This book was released on 2022-11-09 with total page 754 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning and Convolutional Neural Networks for Medical Image Computing written by Le Lu and published by Springer. This book was released on 2017-07-12 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Download or read book Medical Statistics written by Stephen J. Walters and published by John Wiley & Sons. This book was released on 2021-02-01 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5th edition of this popular introduction to statistics for the medical and health sciences has undergone a significant revision, with several new chapters added and examples refreshed throughout the book. Yet it retains its central philosophy to explain medical statistics with as little technical detail as possible, making it accessible to a wide audience. Helpful multi-choice exercises are included at the end of each chapter, with answers provided at the end of the book. Each analysis technique is carefully explained and the mathematics kept to minimum. Written in a style suitable for statisticians and clinicians alike, this edition features many real and original examples, taken from the authors' combined many years' experience of designing and analysing clinical trials and teaching statistics. Students of the health sciences, such as medicine, nursing, dentistry, physiotherapy, occupational therapy, and radiography should find the book useful, with examples relevant to their disciplines. The aim of training courses in medical statistics pertinent to these areas is not to turn the students into medical statisticians but rather to help them interpret the published scientific literature and appreciate how to design studies and analyse data arising from their own projects. However, the reader who is about to design their own study and collect, analyse and report on their own data will benefit from a clearly written book on the subject which provides practical guidance to such issues. The practical guidance provided by this book will be of use to professionals working in and/or managing clinical trials, in academic, public health, government and industry settings, particularly medical statisticians, clinicians, trial co-ordinators. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations.

Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-11-23 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Download or read book Information Processing in Medical Imaging written by Marc Niethammer and published by Springer. This book was released on 2017-06-06 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, held at the Appalachian State University, Boon, NC, USA, in June 2017. The 53 full papers presented in this volume were carefully reviewed and selected from 147 submissions. They were organized in topical sections named: analysis on manifolds; shape analysis; disease diagnosis/progression; brain networks an connectivity; diffusion imaging; quantitative imaging; imaging genomics; image registration; segmentation; general image analysis.

Download or read book Neuroimaging in Dementia written by Frederik Barkhof and published by Springer Science & Business Media. This book was released on 2011-02-11 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This up-to-date, superbly illustrated book is a practical guide to the effective use of neuroimaging in the patient with cognitive decline. It sets out the key clinical and imaging features of the various causes of dementia and directs the reader from clinical presentation to neuroimaging and on to an accurate diagnosis whenever possible. After an introductory chapter on the clinical background, the available "toolbox" of structural and functional neuroimaging techniques is reviewed in detail, including CT, MRI and advanced MR techniques, SPECT and PET, and image analysis methods. The imaging findings in normal ageing are then discussed, followed by a series of chapters that carefully present and analyze the key findings in patients with dementias. Throughout, a practical approach is adopted, geared specifically to the needs of clinicians (neurologists, radiologists, psychiatrists, geriatricians) working in the field of dementia, for whom this book will prove an invaluable resource.

Download or read book MICCAI 2012 Workshop on Multi Atlas Labeling written by Bennett Landman and published by . This book was released on 2012-08-26 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Characterization of anatomical structure through segmentation has become essential for morphological assessment and localizing quantitative measures. Segmentation through registration and atlas label transfer has proven to be a flexible and fruitful approach as efficient, non-rigid image registration methods have become prevalent. Label transfer segmentation using multiple atlases has helped to bring statistical fusion, shape modeling, and meta-analysis techniques to the forefront of segmentation research. Numerous creative approaches have proposed to use atlas information to apply labels to brain anatomy. However, it is difficult to evaluate the relative advantages and limitations of these methods as they have been applied on very different datasets. This workshop provides a snapshot of the current progress in the field through extended discussions and provides researchers an opportunity to characterize their methods on standardized data in a grand challenge.

Download or read book Medical Imaging Informatics written by Alex A.T. Bui and published by Springer Science & Business Media. This book was released on 2009-12-01 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2015 written by Nassir Navab and published by Springer. This book was released on 2015-09-28 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 9349, 9350, and 9351 constitutes the refereed proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, held in Munich, Germany, in October 2015. Based on rigorous peer reviews, the program committee carefully selected 263 revised papers from 810 submissions for presentation in three volumes. The papers have been organized in the following topical sections: quantitative image analysis I: segmentation and measurement; computer-aided diagnosis: machine learning; computer-aided diagnosis: automation; quantitative image analysis II: classification, detection, features, and morphology; advanced MRI: diffusion, fMRI, DCE; quantitative image analysis III: motion, deformation, development and degeneration; quantitative image analysis IV: microscopy, fluorescence and histological imagery; registration: method and advanced applications; reconstruction, image formation, advanced acquisition - computational imaging; modelling and simulation for diagnosis and interventional planning; computer-assisted and image-guided interventions.

Download or read book Machine Learning in Radiation Oncology written by Issam El Naqa and published by Springer. This book was released on 2015-06-19 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​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.

Download or read book Medical Image Registration written by Joseph V. Hajnal and published by CRC Press. This book was released on 2001-06-27 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid

Download or read book Deep Learning for Biomedical Applications written by Utku Kose and published by CRC Press. This book was released on 2021-07-19 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.