handbook of deep learning in biomedical engineering

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Handbook of Deep Learning in Biomedical Engineering
Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Publisher : Academic Press
Release Date : 2020-11-23
ISBN 10 : 0128230479
Pages : 320 pages

Deep learning (DL) is a method of machine learning, running over artificial neural networks, that uses multiple layers to extract high-level features from large amounts of raw data. DL methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of DL and its applications in the field of biomedical engineering. DL has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. DL provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and artificial intelligence techniques such as DL and convolutional neural networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use DL include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic particle imaging, electroencephalography/magnetoencephalography (EE/MEG), optical microscopy and tomography, photoacoustic tomography, electron tomography, and atomic force microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of DL applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer’s, attention deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), tumor prediction, and translational multimodal imaging analysis. Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT. Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis. Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks. Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography. ~

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
Author : E Golden Julie,Y Harold Robinson,S M Jaisakthi
Publisher : Apple Academic Press
Release Date : 2021-08
ISBN 10 : 9781771889988
Pages : 329 pages

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.

Handbook of Artificial Intelligence in Biomedical Engineering
Author : Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G.S. Mahalakshmi
Publisher : CRC Press
Release Date : 2021-03-30
ISBN 10 : 100006767X
Pages : 538 pages

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.

Handbook of Data Science Approaches for Biomedical Engineering
Author : Valentina E. Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
Publisher : Academic Press
Release Date : 2019-11-13
ISBN 10 : 0128183195
Pages : 318 pages

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more

Handbook of Deep Learning Applications
Author : Valentina Emilia Balas,Sanjiban Sekhar Roy,Dharmendra Sharma,Pijush Samui
Publisher : Springer
Release Date : 2019-02-25
ISBN 10 : 3030114791
Pages : 383 pages

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Handbook of Research on Deep Learning Innovations and Trends
Author : Aboul Ella Hassanien,Ashraf Darwish,Chiranji Lal Chowdhary
Publisher : N.A
Release Date : 2019
ISBN 10 : 9781522578628
Pages : 500 pages

"This book explores the application of deep learning in various areas like computer vision, image processing, biometrics, pattern recognition and medical imaging, and other real-world applications"--

Computational Intelligence and Its Applications in Healthcare
Author : Prashant Johri,Jitendra Kumar Verma,Sudip Paul
Publisher : Academic Press
Release Date : 2020-08-01
ISBN 10 : 0128206195
Pages : 264 pages

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare. Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms Presents the latest developments in computational methods in healthcare Bridges the gap between obsolete literature and current literature

The AI Book
Author : Ivana Bartoletti,Anne Leslie,Shân M. Millie
Publisher : John Wiley & Sons
Release Date : 2020-04-09
ISBN 10 : 1119551927
Pages : 304 pages

Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important

Handbook of Research on Biomedical Engineering Education and Advanced Bioengineering Learning: Interdisciplinary Concepts
Author : Abu-Faraj, Ziad O.
Publisher : IGI Global
Release Date : 2012-02-29
ISBN 10 : 146660123X
Pages : 1122 pages

Description based on: v. 2, copyrighted in 2012.

Handbook of Medical Image Computing and Computer Assisted Intervention
Author : S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger
Publisher : Academic Press
Release Date : 2019-10-18
ISBN 10 : 0128165863
Pages : 1072 pages

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

Handbook of Neural Engineering
Author : Metin Akay
Publisher : John Wiley & Sons
Release Date : 2007-01-09
ISBN 10 : 0470068280
Pages : 800 pages

An important new work establishing a foundation for future developments in neural engineering The Handbook of Neural Engineering provides theoretical foundations in computational neural science and engineering and current applications in wearable and implantable neural sensors/probes. Inside, leading experts from diverse disciplinary groups representing academia, industry, and private and government organizations present peer-reviewed contributions on the brain-computer interface, nano-neural engineering, neural prostheses, imaging the brain, neural signal processing, the brain, and neurons. The Handbook of Neural Engineering covers: Neural signal and image processing--the analysis and modeling of neural activity and EEG-related activities using the nonlinear and nonstationary analysis methods, including the chaos, fractal, and time-frequency and time-scale analysis methods--and how to measure functional, physiological, and metabolic activities in the human brain using current and emerging medical imaging technologies Neuro-nanotechnology, artificial implants, and neural prosthesis--the design of multi-electrode arrays to study how the neurons of human and animals encode stimuli, the evaluation of functional changes in neural networks after stroke and spinal cord injuries, and improvements in therapeutic applications using neural prostheses Neurorobotics and neural rehabilitation engineering--the recent developments in the areas of biorobotic system, biosonar head, limb kinematics, and robot-assisted activity to improve the treatment of elderly subjects at the hospital and home, as well as the interactions of the neuron chip, neural information processing, perception and neural dynamics, learning memory and behavior, biological neural networks, and neural control

Deep Learning Techniques for Biomedical and Health Informatics
Author : Dr. Basant Agarwal,Valentina E. Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma
Publisher : Academic Press
Release Date : 2020-01-14
ISBN 10 : 0128190620
Pages : 367 pages

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