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Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics
A Book

by Dr. Basant Agarwal,Valentina E. Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma

  • Publisher : Academic Press
  • Release : 2020-01-14
  • Pages : 367
  • ISBN : 0128190620
  • Language : En, Es, Fr & De
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Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis

Deep Learning in Biomedical and Health Informatics

Deep Learning in Biomedical and Health Informatics
Current Applications and Possibilities

by Taylor & Francis Group

  • Publisher : CRC Press
  • Release : 2021-08-20
  • Pages : 232
  • ISBN : 9780367726041
  • Language : En, Es, Fr & De
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This book provides a proficient guide on the relationship between AI and healthcare and how AI is changing all aspects of the health care industry. It also covers how deep learning will help in diagnosis and prediction of disease spread. The editors present a comprehensive review of research, applying deep learning in health informatics, in the fields of medical imaging, electronic health records, genomics, sensing, and also highlights various challenges in applying the deep learning in health care. The Book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments and applications of deep learning and to solve emerging problems in health care and domain. This book is intended for computer scientists, biomedical engineers, and health care professionals researching and developing deep learning techniques.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
A Book

by E Golden Julie,Y Harold Robinson,S M Jaisakthi

  • Publisher : Apple Academic Press
  • Release : 2021-08
  • Pages : 329
  • ISBN : 9781771889988
  • Language : En, Es, Fr & De
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This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat the patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. The volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.

Machine Learning and the Internet of Medical Things in Healthcare

Machine Learning and the Internet of Medical Things in Healthcare
A Book

by Krishna Kant Singh,Mohamed Elhoseny,Akansha Singh,Ahmed A. Elngar

  • Publisher : Academic Press
  • Release : 2021-04-26
  • Pages : 290
  • ISBN : 012823217X
  • Language : En, Es, Fr & De
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Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies

Trends in Deep Learning Methodologies

Trends in Deep Learning Methodologies
Algorithms, Applications, and Systems

by Vincenzo Piuri,Sandeep Raj,Angelo Genovese,Rajshree Srivastava

  • Publisher : Academic Press
  • Release : 2020-12-01
  • Pages : 306
  • ISBN : 0128232684
  • Language : En, Es, Fr & De
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Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. Provides insights into the theory, algorithms, implementation and the application of deep learning techniques Covers a wide range of applications of deep learning across smart healthcare and smart engineering Investigates the development of new models and how they can be exploited to find appropriate solutions

Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics

Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics
ICEBEHI 2020, 8–9 October, Surabaya, Indonesia

by Triwiyanto

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 9813369264
  • Language : En, Es, Fr & De
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The Importance of Health Informatics in Public Health during a Pandemic

The Importance of Health Informatics in Public Health during a Pandemic
A Book

by J. Mantas,A. Hasman,M.S. Househ

  • Publisher : IOS Press
  • Release : 2020-07-24
  • Pages : 520
  • ISBN : 1643680935
  • Language : En, Es, Fr & De
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The COVID-19 pandemic has increased the focus on health informatics and healthcare technology for policy makers and healthcare professionals worldwide. This book contains the 110 papers (from 160 submissions) accepted for the 18th annual International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH 2020), held virtually in Athens, Greece, from 3 – 5 July 2020. The conference attracts scientists working in the field of Biomedical and Health Informatics from all continents, and this year it was held as a Virtual Conference, by means of teleconferencing, due to the COVID-19 pandemic and the consequent lockdown in many countries around the world. The call for papers for the conference started in December 2019, when signs of the new virus infection were not yet evident, so early submissions were on the usual topics as announced. But papers submitted after mid-March were mostly focused on the first results of the pandemic analysis with respect to informatics in different countries and with different perspectives of the spread of the virus and its influence on public health across the world. This book therefore includes papers on the topic of the COVID-19 pandemic in relation to informatics reporting from hospitals and institutions from around the world, including South Korea, Europe, and the USA. The book encompasses the field of biomedical and health informatics in a very broad framework, and the timely inclusion of papers on the current pandemic will make it of particular interest to all those involved in the provision of healthcare everywhere.

Smart Computational Intelligence in Biomedical and Health Informatics

Smart Computational Intelligence in Biomedical and Health Informatics
A Book

by Taylor & Francis Group

  • Publisher : CRC Press
  • Release : 2021-08-26
  • Pages : 216
  • ISBN : 9780367624125
  • Language : En, Es, Fr & De
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Smart Computational Intelligence in Biomedical and Health Informatics presents state-of-art innovations, research, design, and implementation of methodological and algorithmic solutions to data processing problems, designing, including analysis of evolving trends in health informatics and computer-aided diagnosis. Further, it describes practical, applications-led research on the use of methods and devices in clinical diagnosis, disease prevention, patient monitoring and management. It covers simulation and modeling, measurement and control, analysis, information extraction and monitoring of physiological data in clinical medicine and the biological sciences. Covers evolutionary approaches to solve optimization problems in biomedical engineering. Discusses IoT, Cloud computing, data analytics in healthcare informatics. Provides computational intelligence-based solution for diagnosis of diseases. Reviews modelling and simulations in designing of biomedical equipment. Promotes machine learning based approaches to improvements in biomedical engineering problems. This book is aimed at researchers, graduate students in healthcare, biomedical engineering, health informatics, computational intelligence, and machine learning.

Medical Imaging

Medical Imaging
Artificial Intelligence, Image Recognition, and Machine Learning Techniques

by K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey

  • Publisher : CRC Press
  • Release : 2019-08-20
  • Pages : 238
  • ISBN : 0429639325
  • Language : En, Es, Fr & De
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The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Signal Processing Techniques for Computational Health Informatics

Signal Processing Techniques for Computational Health Informatics
A Book

by Md Atiqur Rahman Ahad,Mosabber Uddin Ahmed

  • Publisher : Springer Nature
  • Release : 2020-10-07
  • Pages : 334
  • ISBN : 3030549321
  • Language : En, Es, Fr & De
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This book focuses on signal processing techniques used in computational health informatics. As computational health informatics is the interdisciplinary study of the design, development, adoption and application of information and technology-based innovations, specifically, computational techniques that are relevant in health care, the book covers a comprehensive and representative range of signal processing techniques used in biomedical applications, including: bio-signal origin and dynamics, sensors used for data acquisition, artefact and noise removal techniques, feature extraction techniques in the time, frequency, time–frequency and complexity domain, and image processing techniques in different image modalities. Moreover, it includes an extensive discussion of security and privacy challenges, opportunities and future directions for computational health informatics in the big data age, and addresses the incorporation of recent techniques from the areas of artificial intelligence, deep learning and human–computer interaction. The systematic analysis of the state-of-the-art techniques covered here helps to further our understanding of the physiological processes involved and expandour capabilities in medical diagnosis and prognosis. In closing, the book, the first of its kind, blends state-of-the-art theory and practices of signal processing techniques inthe health informatics domain with real-world case studies building on those theories. As a result, it can be used as a text for health informatics courses to provide medics with cutting-edge signal processing techniques, or to introducehealth professionals who are already serving in this sector to some of the most exciting computational ideas that paved the way for the development of computational health informatics.

Handbook of Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering
A Book

by Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G. S. Mahalakshmi

  • Publisher : Apple Academic Press
  • Release : 2020-12-15
  • Pages : 622
  • ISBN : 9781771889209
  • Language : En, Es, Fr & De
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"Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts. Topics include: Security and privacy issues in biomedical AI systems and potential solutions Healthcare applications using biomedical AI systems Machine learning in biomedical engineering Live patient monitoring systems Semantic annotation of healthcare data This book presents a broad exploration of biomedical systems using artificial intelligence techniques with detailed coverage of the applications, techniques, algorithms, platforms, and tools in biomedical AI systems. This book will benefit researchers, medical and industry practitioners, academicians, and students"--

Health Informatics Vision: From Data via Information to Knowledge

Health Informatics Vision: From Data via Information to Knowledge
A Book

by J. Mantas,A. Hasman,P. Gallos

  • Publisher : IOS Press
  • Release : 2019-08-06
  • Pages : 420
  • ISBN : 1614999872
  • Language : En, Es, Fr & De
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The latest developments in data, informatics and technology continue to enable health professionals and informaticians to improve healthcare for the benefit of patients everywhere. This book presents full papers from ICIMTH 2019, the 17th International Conference on Informatics, Management and Technology in Healthcare, held in Athens, Greece from 5 to 7 July 2019. Of the 150 submissions received, 95 were selected for presentation at the conference following review and are included here. The conference focused on increasing and improving knowledge of healthcare applications spanning the entire spectrum from clinical and health informatics to public health informatics as applied in the healthcare domain. The field of biomedical and health informatics is examined in a very broad framework, presenting the research and application outcomes of informatics from cell to population and exploring a number of technologies such as imaging, sensors, and biomedical equipment, together with management and organizational aspects including legal and social issues. Setting research priorities in health informatics is also addressed. Providing an overview of the latest developments in health informatics, the book will be of interest to all those working in the field.

Precision Medicine Powered by pHealth and Connected Health

Precision Medicine Powered by pHealth and Connected Health
ICBHI 2017, Thessaloniki, Greece, 18-21 November 2017

by Nicos Maglaveras,Ioanna Chouvarda,Paulo de Carvalho

  • Publisher : Springer
  • Release : 2017-11-16
  • Pages : 269
  • ISBN : 9811074194
  • Language : En, Es, Fr & De
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This volume presents the proceedings of the 3rd ICBHI which took place in Thessaloniki on 18-21 November, 2017.The area of biomedical and health informatics is exploding at all scales. The developments in the areas of medical devices, eHealth and personalized health as enabling factors for the evolution of precision medicine are quickly developing and demand the development of new scaling tools, integration frameworks and methodologies.

Deep Learning and Edge Computing Solutions for High Performance Computing

Deep Learning and Edge Computing Solutions for High Performance Computing
A Book

by A. Suresh,Sara Paiva

  • Publisher : Springer Nature
  • Release : 2021-01-27
  • Pages : 279
  • ISBN : 3030602656
  • Language : En, Es, Fr & De
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This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology.

Terahertz Biomedical and Healthcare Technologies

Terahertz Biomedical and Healthcare Technologies
Materials to Devices

by Amit Banerjee,Basabi Chakraborty,Hiroshi Inokawa,Jitendra Nath Roy

  • Publisher : Elsevier
  • Release : 2020-08-11
  • Pages : 262
  • ISBN : 0128185570
  • Language : En, Es, Fr & De
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Terahertz Biomedical and Healthcare Technologies: Materials to Devices reviews emerging advances in terahertz biomedical and healthcare technologies, including advances in fundamental materials science research, device design and fabrication, applications, and challenges and opportunities for improved performance. In addition, the improvement of materials, optical elements, and measuring techniques are also explored. Other sections cover the design and development of wide bandgap semiconductors for terahertz device applications, including their physics, device modeling, characterization and fabrication concepts. Finally, the book touches on potential defense, medical imaging, internet of things, and the machine learning applications of terahertz technologies. Reviews the latest advances in the fundamental and applied research of terahertz technologies, covering key topics in materials science, biomedical engineering and healthcare informatics Includes applications of terahertz technologies in medical imaging, diagnosis and treatment Provides readers with an understanding of the machine learning, pattern recognition, and data analytics research utilized to enhance the effectiveness of terahertz technologies

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics
A Book

by Yanqing Zhang,Jagath C. Rajapakse

  • Publisher : John Wiley & Sons
  • Release : 2009-02-23
  • Pages : 400
  • ISBN : 9780470397411
  • Language : En, Es, Fr & De
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An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

Computational Intelligence for Machine Learning and Healthcare Informatics

Computational Intelligence for Machine Learning and Healthcare Informatics
A Book

by Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey

  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 2020-06-22
  • Pages : 346
  • ISBN : 3110649276
  • Language : En, Es, Fr & De
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This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics
A Book

by Pradeep Nijalingappa,Sandeep Kautish,Sheng Lung Peng

  • Publisher : Academic Press
  • Release : 2021-06-01
  • Pages : 332
  • ISBN : 0128220449
  • Language : En, Es, Fr & De
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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents how these emerging areas are changing the world of data utilization, especially in clinical healthcare. Various techniques, methodologies and algorithms are presented in a structured manner to assist physicians in the precision care of patients and help biomedical engineers and computers scientists understand the impact of these techniques on healthcare analytics. Sections cover Big Data aspects, i.e., healthcare Decision Support Systems and Analytics related topics, focus on current frameworks and applications of Deep Learning and Machine Learning, and provide an outlook on future directions. The entire book takes a case study approach, providing a wealth of real-world case studies that act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables and graphs with algorithms and computational methods for developing new applications in healthcare informatics Presents a unique case study approach that provides readers with insights for practical clinical implementations

Machine Learning for Health Informatics

Machine Learning for Health Informatics
State-of-the-Art and Future Challenges

by Andreas Holzinger

  • Publisher : Springer
  • Release : 2016-12-09
  • Pages : 481
  • ISBN : 3319504789
  • Language : En, Es, Fr & De
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Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
A Book

by Guorong Wu,Dinggang Shen,Mert Sabuncu

  • Publisher : Academic Press
  • Release : 2016-08-11
  • Pages : 512
  • ISBN : 0128041145
  • Language : En, Es, Fr & De
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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