Medical Image Recognition Segmentation And Parsing

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• Author : S. Kevin Zhou
• Publisher : Academic Press
• Release Date : 2015-12-11
• Total pages : 542
• ISBN : 9780128026762
• File Size : 27,8 Mb
• Total Download : 530
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Download Medical Image Recognition, Segmentation and Parsing in PDF, Epub, and Kindle

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets Includes algorithms for recognizing and parsing of known anatomies for practical applications

• Author : S. Kevin Zhou
• Publisher : Academic Press
• Release Date : 2015-12-08
• Total pages : 0
• ISBN : 0128025816
• File Size : 54,5 Mb
• Total Download : 947
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Download Medical Image Recognition, Segmentation and Parsing in PDF, Epub, and Kindle

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms

• Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
• Publisher : Academic Press
• Release Date : 2017-01-18
• Total pages : 458
• ISBN : 9780128104095
• File Size : 22,8 Mb
• Total Download : 954
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Download Deep Learning for Medical Image Analysis in PDF, Epub, and Kindle

Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes 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 Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

• Author : Bjoern Menze,Georg Langs,Zhuowen Tu,Antonio Criminisi
• Publisher : Springer
• Release Date : 2011-02-02
• Total pages : 226
• ISBN : 9783642184215
• File Size : 36,5 Mb
• Total Download : 496
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Download Medical Computer Vision in PDF, Epub, and Kindle

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2010, held in Beijing, China, in September 2010 as a satellite event of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2010. The 10 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 38 initial submissions. The papers explore the use of modern image recognition technology in tasks such as semantic anatomy parsing, automatic segmentation and quantification, anomaly detection and categorization, data harvesting, semantic navigation and visualization, data organization and clustering, and general-purpose automatic understanding of medical images.

• Author : S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger
• Publisher : Academic Press
• Release Date : 2019-10-18
• Total pages : 1074
• ISBN : 9780128165867
• File Size : 48,7 Mb
• Total Download : 813
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Download Handbook of Medical Image Computing and Computer Assisted Intervention in PDF, Epub, and Kindle

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention

• Author : Qian Wang,Yinghuan Shi,Heung-Il Suk,Kenji Suzuki
• Publisher : Springer
• Release Date : 2017-09-06
• Total pages : 391
• ISBN : 9783319673899
• File Size : 15,6 Mb
• Total Download : 255
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Download Machine Learning in Medical Imaging in PDF, Epub, and Kindle

This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

• Author : Rabeb Ben Kahla,Abdelwahed Barkaoui,Tarek Merzouki
• Publisher : John Wiley & Sons
• Release Date : 2020-01-02
• Total pages : 200
• ISBN : 9781786305183
• File Size : 54,9 Mb
• Total Download : 788
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Download Finite Element Method and Medical Imaging Techniques in Bone Biomechanics in PDF, Epub, and Kindle

Digital models based on data from medical images have recently become widespread in the field of biomechanics. This book summarizes medical imaging techniques and processing procedures, both of which are necessary for creating bone models with finite element methods. Chapter 1 introduces the main principles and the application of the most commonly used medical imaging techniques. Chapter 2 describes the major methods and steps of medical image analysis and processing. Chapter 3 presents a brief review of recent studies on reconstructed finite element bone models, based on medical images. Finally, Chapter 4 reveals the digital results obtained for the main bone sites that have been targeted by finite element modeling in recent years.

• Author : Antonio Criminisi,J Shotton
• Publisher : Springer Science & Business Media
• Release Date : 2013-01-30
• Total pages : 368
• ISBN : 9781447149293
• File Size : 38,7 Mb
• Total Download : 238
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Download Decision Forests for Computer Vision and Medical Image Analysis in PDF, Epub, and Kindle

This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.

• Author : Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
• Publisher : Springer
• Release Date : 2017-07-12
• Total pages : 326
• ISBN : 9783319429991
• File Size : 39,7 Mb
• Total Download : 881
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Download Deep Learning and Convolutional Neural Networks for Medical Image Computing in PDF, Epub, and Kindle

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.

• Author : Siddhartha Bhattacharyya,Paramartha Dutta,Sourav De,Goran Klepac
• Publisher : Springer
• Release Date : 2016-11-12
• Total pages : 321
• ISBN : 9783319472232
• File Size : 47,6 Mb
• Total Download : 539
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Download Hybrid Soft Computing for Image Segmentation in PDF, Epub, and Kindle

This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.

• Author : Kaijian Xia,Yizhang Jiang,Yu-Dong Zhang,Mohammad Khosravi,Yuanpeng Zhang
• Publisher : Frontiers Media SA
• Release Date : 2022-11-09
• Total pages : 754
• ISBN : 9782832504628
• File Size : 42,6 Mb
• Total Download : 214
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Download Advanced Computational Intelligence Methods for Processing Brain Imaging Data in PDF, Epub, and Kindle

PDF book entitled Advanced Computational Intelligence Methods for Processing Brain Imaging Data written by Kaijian Xia,Yizhang Jiang,Yu-Dong Zhang,Mohammad Khosravi,Yuanpeng Zhang and published by Frontiers Media SA which was released on 2022-11-09 with total hardcover pages 754, the book become popular and critical acclaim.

• Author : Anne L. Martel,Purang Abolmaesumi,Danail Stoyanov,Diana Mateus,Maria A. Zuluaga,S. Kevin Zhou,Daniel Racoceanu,Leo Joskowicz
• Publisher : Springer Nature
• Release Date : 2020-10-02
• Total pages : 785
• ISBN : 9783030597139
• File Size : 54,5 Mb
• Total Download : 967
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Download Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 in PDF, Epub, and Kindle

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

• Author : Xavier Pennec,Stefan Sommer,Tom Fletcher
• Publisher : Academic Press
• Release Date : 2019-09
• Total pages : 634
• ISBN : 9780128147252
• File Size : 37,8 Mb
• Total Download : 111
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Download Riemannian Geometric Statistics in Medical Image Analysis in PDF, Epub, and Kindle

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: - The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs - Applications of statistics on manifolds and shape spaces in medical image computing - Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. - A complete reference covering both the foundations and state-of-the-art methods - Edited and authored by leading researchers in the field - Contains theory, examples, applications, and algorithms - Gives an overview of current research challenges and future applications

• Author : Haofu Liao,S. Kevin Zhou,Jiebo Luo
• Publisher : Academic Press
• Release Date : 2022-09-01
• Total pages : 266
• ISBN : 9780128244036
• File Size : 27,9 Mb
• Total Download : 629
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Download Deep Network Design for Medical Image Computing in PDF, Epub, and Kindle

Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems. Explains design principles of deep learning techniques for MIC Contains cutting-edge deep learning research on MIC Covers a broad range of MIC tasks, including the classification, detection, segmentation, registration, reconstruction and synthesis of medical images

• Author : Hien Van Nguyen,Ronald Summers,Rama Chellappa
• Publisher : Academic Press
• Release Date : 2022-09-30
• Total pages : 430
• ISBN : 9780323998529
• File Size : 20,9 Mb
• Total Download : 790
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Download Meta Learning With Medical Imaging and Health Informatics Applications in PDF, Epub, and Kindle

Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks’ fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks. This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. The book comes with a GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly. First book on applying Meta Learning to medical imaging Pioneers in the field as contributing authors to explain the theory and its development Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly

• Author : Ninon Burgos,David Svoboda
• Publisher : Academic Press
• Release Date : 2022-06-30
• Total pages : 674
• ISBN : 9780128243503
• File Size : 37,7 Mb
• Total Download : 808
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Download Biomedical Image Synthesis and Simulation in PDF, Epub, and Kindle

Biomedical Image Synthesis and Simulations: Methods and Applications presents the latest on basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. Sections introduce and describe the simulation and synthesis methods that were developed and successfully used within the last twenty years and give examples of successful applications of these methods. As the book provides a survey of all the commonly established approaches and more recent deep learning methods, it is highly suitable for graduate students and researchers in medical and biomedical imaging. Gives state-of-the-art methods in (bio)medical image synthesis Explains the principles (background) of image synthesis methods Presents the main applications of biomedical image synthesis methods

• Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
• Publisher : Academic Press
• Release Date : 2016-08-11
• Total pages : 512
• ISBN : 9780128041147
• File Size : 41,7 Mb
• Total Download : 907
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Download Machine Learning and Medical Imaging in PDF, Epub, and Kindle

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

• Author : Bernhard Preim,Renata Raidou,Noeska Smit,Kai Lawonn
• Publisher : Elsevier
• Release Date : 2023-05-15
• Total pages : 570
• ISBN : 9780128231067
• File Size : 39,7 Mb
• Total Download : 766
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Download Visualization, Visual Analytics and Virtual Reality in Medicine in PDF, Epub, and Kindle

Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understanding of actual user needs as well as technological possibilities. The book includes user research, for example, task and requirement analysis, visualization design and algorithmic ideas without going into the details of implementation. This reference will be suitable for researchers and students in visualization and visual analytics in medicine and healthcare, medical image analysis scientists and biomedical engineers in general. Visualization and visual analytics have become prevalent in public health and clinical medicine, medical flow visualization, multimodal medical visualization and virtual reality in medical education and rehabilitation. Relevant applications now include digital pathology, virtual anatomy and computer-assisted radiation treatment planning. Combines visualization, virtual reality and analytics Written by leading researchers in the field Gives the latest state-of-the-art techniques and applications

• Author : Adrien Depeursinge,Omar S Al-Kadi,J.Ross Mitchell
• Publisher : Academic Press
• Release Date : 2017-08-25
• Total pages : 430
• ISBN : 9780128123218
• File Size : 9,8 Mb
• Total Download : 283
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Download Biomedical Texture Analysis in PDF, Epub, and Kindle

Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images. Defines biomedical texture precisely and describe how it is different from general texture information considered in computer vision Defines the general problem to translate 2D and 3D texture patterns from biomedical images to visually and biologically relevant measurements Describes, using intuitive concepts, how the most popular biomedical texture analysis approaches (e.g., gray-level matrices, fractals, wavelets, deep convolutional neural networks) work, what they have in common, and how they are different Identifies the strengths, weaknesses, and current challenges of existing methods including both handcrafted and learned representations, as well as deep learning. The goal is to establish foundations for building the next generation of biomedical texture operators Showcases applications where biomedical texture analysis has succeeded and failed Provides details on existing, freely available texture analysis software, helping experts in medicine or biology develop and test precise research hypothesis

• Author : Emanuele Trucco,Tom MacGillivray,Yanwu Xu
• Publisher : Academic Press
• Release Date : 2019-11-25
• Total pages : 502
• ISBN : 9780081028179
• File Size : 41,6 Mb
• Total Download : 219
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Download Computational Retinal Image Analysis in PDF, Epub, and Kindle

Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies

• Author : Lan Zou
• Publisher : Elsevier
• Release Date : 2022-11-05
• Total pages : 404
• ISBN : 9780323903707
• File Size : 31,8 Mb
• Total Download : 894
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Download Meta-Learning in PDF, Epub, and Kindle

Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications. A comprehensive overview of state-of-the-art meta-learning techniques and methods associated with deep neural networks together with a broad range of application areas Coverage of nearly 200 state-of-the-art meta-learning algorithms, which are promoted by premier global AI conferences and journals, and 300 to 450 pieces of key research Systematic and detailed exploration of the most crucial state-of-the-art meta-learning algorithm mechanisms: model-based, metric-based, and optimization-based Provides solutions to the limitations of using deep learning and/or machine learning methods, particularly with small sample sizes and unlabeled data Gives an understanding of how meta-learning acts as a stepping stone to Artificial General Intelligence in 39 categories of tasks from 11 real-world application fields