Deciphering And Modelling The Action Of Immune Cells Using Highly Multiplexed Imaging And Deep Learning Techniques

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Deciphering and Modelling the Action of Immune Cells Using Highly Multiplexed Imaging and Deep Learning Techniques

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Author : Clinton Reid
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

Book Synopsis Deciphering and Modelling the Action of Immune Cells Using Highly Multiplexed Imaging and Deep Learning Techniques by : Clinton Reid

Download or read book Deciphering and Modelling the Action of Immune Cells Using Highly Multiplexed Imaging and Deep Learning Techniques written by Clinton Reid and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cells of the immune system are capable of responding to foreign antigen, promoting host defense while limiting damage to host tissues, through an act known as self-tolerance. T cells, their activation and their effector roles are of particular interest due to their prominent roles in antigen discrimination and subsequent cell-mediated immunity. However, there are diverse effector T cell types interacting to regulate the immune response. Understanding the mechanisms by which intercellular interactions exert precise control over the immune system is a crucial step in elucidating the manner in which the immune system behaves during infection, health, or chronic disease. Multiplexed imaging is a beneficial tool that is used to visualize distinct cell types and functional states directly in tissues. This technology is particularly important for understanding how cells organize spatially to enforce this boundary between host-protective responses and autoimmunity. Therefore, it is valuable to image interacting cells in highly-multiplexed images. In order to do this, it has become increasingly important to increase the number of biomarkers that one can record in a single tissue section at a time. Here, I summarize our efforts to employ imaging and deep learning tools to analyze the structure of the immune system, ending with a critical insight regarding our cell segmentation models alongside an experimental workflow and pipeline that will allow even more to be revealed about the mechanisms of control that exist within the immune system. Current methods for acquiring highly multiplexed images are somewhat time-consuming and labour-intensive while computational methods for analyzing these images and identifying relevant spatial patterns are lacking. We seek to improve and simplify our current multiplexing capabilities by eventually coupling fluorescence lifetime with fluorescence intensity measurements-two distinct imaging modalities. Moreover, we aim to develop new computational pipelines to aid in down-stream image analysis and identify new spatial motifs that control immune response in tissues.

Mathematical Modeling of the Immune System in Homeostasis, Infection and Disease

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Author : Gennady Bocharov
Publisher : Frontiers Media SA
ISBN 13 : 2889634612
Total Pages : 278 pages
Book Rating : 4.8/5 (896 download)

Book Synopsis Mathematical Modeling of the Immune System in Homeostasis, Infection and Disease by : Gennady Bocharov

Download or read book Mathematical Modeling of the Immune System in Homeostasis, Infection and Disease written by Gennady Bocharov and published by Frontiers Media SA. This book was released on 2020-02-24 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The immune system provides the host organism with defense mechanisms against invading pathogens and tumor development and it plays an active role in tissue and organ regeneration. Deviations from the normal physiological functioning of the immune system can lead to the development of diseases with various pathologies including autoimmune diseases and cancer. Modern research in immunology is characterized by an unprecedented level of detail that has progressed towards viewing the immune system as numerous components that function together as a whole network. Currently, we are facing significant difficulties in analyzing the data being generated from high-throughput technologies for understanding immune system dynamics and functions, a problem known as the ‘curse of dimensionality’. As the mainstream research in mathematical immunology is based on low-resolution models, a fundamental question is how complex the mathematical models should be? To respond to this challenging issue, we advocate a hypothesis-driven approach to formulate and apply available mathematical modelling technologies for understanding the complexity of the immune system. Moreover, pure empirical analyses of immune system behavior and the system’s response to external perturbations can only produce a static description of the individual components of the immune system and the interactions between them. Shifting our view of the immune system from a static schematic perception to a dynamic multi-level system is a daunting task. It requires the development of appropriate mathematical methodologies for the holistic and quantitative analysis of multi-level molecular and cellular networks. Their coordinated behavior is dynamically controlled via distributed feedback and feedforward mechanisms which altogether orchestrate immune system functions. The molecular regulatory loops inherent to the immune system that mediate cellular behaviors, e.g. exhaustion, suppression, activation and tuning, can be analyzed using mathematical categories such as multi-stability, switches, ultra-sensitivity, distributed system, graph dynamics, or hierarchical control. GB is supported by the Russian Science Foundation (grant 18-11-00171). AM is also supported by grants from the Spanish Ministry of Economy, Industry and Competitiveness and FEDER grant no. SAF2016-75505-R, the “María de Maeztu” Programme for Units of Excellence in R&D (MDM-2014-0370) and the Russian Science Foundation (grant 18-11-00171).

Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

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Author : Le Lu
Publisher : Springer Nature
ISBN 13 : 3030139697
Total Pages : 461 pages
Book Rating : 4.0/5 (31 download)

Book Synopsis Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics by : Le Lu

Download or read book Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics written by Le Lu and published by Springer Nature. This book was released on 2019-09-19 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.

Preclinical Models and Emerging Technologies to Study the Effects of the Tumor Microenvironment on Cancer Heterogeneity and Drug Resistance

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Author : Giulia Adriani
Publisher : Frontiers Media SA
ISBN 13 : 2832537030
Total Pages : 171 pages
Book Rating : 4.8/5 (325 download)

Book Synopsis Preclinical Models and Emerging Technologies to Study the Effects of the Tumor Microenvironment on Cancer Heterogeneity and Drug Resistance by : Giulia Adriani

Download or read book Preclinical Models and Emerging Technologies to Study the Effects of the Tumor Microenvironment on Cancer Heterogeneity and Drug Resistance written by Giulia Adriani and published by Frontiers Media SA. This book was released on 2023-10-26 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advances in Deep Learning for Medical Image Analysis

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Author : Archana Mire
Publisher : CRC Press
ISBN 13 : 1000575950
Total Pages : 169 pages
Book Rating : 4.0/5 (5 download)

Book Synopsis Advances in Deep Learning for Medical Image Analysis by : Archana Mire

Download or read book Advances in Deep Learning for Medical Image Analysis written by Archana Mire and published by CRC Press. This book was released on 2022-04-26 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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Author : Anne L. Martel
Publisher : Springer Nature
ISBN 13 : 3030597229
Total Pages : 842 pages
Book Rating : 4.0/5 (35 download)

Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 by : Anne L. Martel

Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 written by Anne L. Martel and published by Springer Nature. This book was released on 2020-10-02 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Visualizing Immunity

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Author : Dorian McGavern
Publisher : Springer Science & Business Media
ISBN 13 : 3540938648
Total Pages : 299 pages
Book Rating : 4.5/5 (49 download)

Book Synopsis Visualizing Immunity by : Dorian McGavern

Download or read book Visualizing Immunity written by Dorian McGavern and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers have used a variety of techniques over the past century to gain fun- mental insights in the field of immunology and, as technology has advanced, so too has the ability of researchers to delve deeper into the biological mechanics of immunity. The immune system is exceedingly complex and must patrol the entire body to protect us from foreign invaders. This requires the immune system to be highly mobile and adaptable - able to respond to diverse microbial challenges while maintaining the ability to distinguish self from a foreign invader. This latter feature is of great importance because the immune system is equipped with toxic mediators, and a failure in self/non-self discrimination can result in serious diseases. Fortunately, in most cases, the immune system operates within the framework of its elegant design and protects us from diverse microbial challenges without initiating disease. Because the immune system is not confined to a single tissue, a comprehensive understanding of immunity requires that research be conducted at the molecular, cellular, and systems level. Immune cells often find customized solutions to h- dling microbial insults that depend on the tissue(s) in which the pathogen is found.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

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Author : E. Golden Julie
Publisher : CRC Press
ISBN 13 : 1000370496
Total Pages : 366 pages
Book Rating : 4.0/5 (3 download)

Book Synopsis Handbook of Deep Learning in Biomedical Engineering and Health Informatics by : E. Golden Julie

Download or read book Handbook of Deep Learning in Biomedical Engineering and Health Informatics written by E. Golden Julie and published by CRC Press. This book was released on 2021-09-22 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 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. This 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.

Deep Learning in Medical Image Analysis

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Author : R. Indrakumari
Publisher : CRC Press
ISBN 13 : 104004798X
Total Pages : 197 pages
Book Rating : 4.0/5 (4 download)

Book Synopsis Deep Learning in Medical Image Analysis by : R. Indrakumari

Download or read book Deep Learning in Medical Image Analysis written by R. Indrakumari and published by CRC Press. This book was released on 2024-07-10 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed as a reference text and provides a comprehensive overview of conceptual and practical knowledge about deep learning in medical image processing techniques. The post-pandemic situation teaches us the importance of doctors, medical analysis, and diagnosis of diseases in a rapid manner. This book provides a snapshot of the state of current research between deep learning, medical image processing, and health care with special emphasis on saving human life. The chapters cover a range of advanced technologies related to patient health monitoring, predicting diseases from genomic data, detecting artefactual events in vital signs monitoring data, and managing chronic diseases. This book Delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field Presents key principles by implementing algorithms from scratch and using simple MATLAB®/Octave scripts with image data Provides an overview of the physics of medical image processing alongside discussing image formats and data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, and tomographic reconstruction Highlights the new potential applications of machine learning techniques to the solution of important problems in biomedical image applications This book is for students, scholars, and professionals of biomedical technology and healthcare data analytics.

Deep Learning in Biomedical and Health Informatics

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Author : M. A. Jabbar
Publisher : CRC Press
ISBN 13 : 1000429121
Total Pages : 251 pages
Book Rating : 4.0/5 (4 download)

Book Synopsis Deep Learning in Biomedical and Health Informatics by : M. A. Jabbar

Download or read book Deep Learning in Biomedical and Health Informatics written by M. A. Jabbar and published by CRC Press. This book was released on 2021-09-27 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the 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, and sensing, and highlights various challenges in applying deep learning in health care. This 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 healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

Machine Learning and Deep Learning Techniques for Medical Image Recognition

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Author : Ben Othman Soufiene
Publisher : CRC Press
ISBN 13 : 1003805671
Total Pages : 270 pages
Book Rating : 4.0/5 (38 download)

Book Synopsis Machine Learning and Deep Learning Techniques for Medical Image Recognition by : Ben Othman Soufiene

Download or read book Machine Learning and Deep Learning Techniques for Medical Image Recognition written by Ben Othman Soufiene and published by CRC Press. This book was released on 2023-12-01 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of 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 and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.

Application of Radiomics in Understanding Tumor Biological Behaviors and Treatment Response

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Author : Sweet Ping Ng
Publisher : Frontiers Media SA
ISBN 13 : 2832533973
Total Pages : 216 pages
Book Rating : 4.8/5 (325 download)

Book Synopsis Application of Radiomics in Understanding Tumor Biological Behaviors and Treatment Response by : Sweet Ping Ng

Download or read book Application of Radiomics in Understanding Tumor Biological Behaviors and Treatment Response written by Sweet Ping Ng and published by Frontiers Media SA. This book was released on 2023-09-19 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Systems Medicine

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Author :
Publisher : Academic Press
ISBN 13 : 0128160780
Total Pages : 1571 pages
Book Rating : 4.1/5 (281 download)

Book Synopsis Systems Medicine by :

Download or read book Systems Medicine written by and published by Academic Press. This book was released on 2020-08-24 with total page 1571 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technological advances in generated molecular and cell biological data are transforming biomedical research. Sequencing, multi-omics and imaging technologies are likely to have deep impact on the future of medical practice. In parallel to technological developments, methodologies to gather, integrate, visualize and analyze heterogeneous and large-scale data sets are needed to develop new approaches for diagnosis, prognosis and therapy. Systems Medicine: Integrative, Qualitative and Computational Approaches is an innovative, interdisciplinary and integrative approach that extends the concept of systems biology and the unprecedented insights that computational methods and mathematical modeling offer of the interactions and network behavior of complex biological systems, to novel clinically relevant applications for the design of more successful prognostic, diagnostic and therapeutic approaches. This 3 volume work features 132 entries from renowned experts in the fields and covers the tools, methods, algorithms and data analysis workflows used for integrating and analyzing multi-dimensional data routinely generated in clinical settings with the aim of providing medical practitioners with robust clinical decision support systems. Importantly the work delves into the applications of systems medicine in areas such as tumor systems biology, metabolic and cardiovascular diseases as well as immunology and infectious diseases amongst others. This is a fundamental resource for biomedical students and researchers as well as medical practitioners who need to need to adopt advances in computational tools and methods into the clinical practice. Encyclopedic coverage: ‘one-stop’ resource for access to information written by world-leading scholars in the field of Systems Biology and Systems Medicine, with easy cross-referencing of related articles to promote understanding and further research Authoritative: the whole work is authored and edited by recognized experts in the field, with a range of different expertise, ensuring a high quality standard Digitally innovative: Hyperlinked references and further readings, cross-references and diagrams/images will allow readers to easily navigate a wealth of information

Machine Learning and Mathematical Models for Single-Cell Data Analysis

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Author : Le Ou-Yang
Publisher : Frontiers Media SA
ISBN 13 : 2832501842
Total Pages : 118 pages
Book Rating : 4.8/5 (325 download)

Book Synopsis Machine Learning and Mathematical Models for Single-Cell Data Analysis by : Le Ou-Yang

Download or read book Machine Learning and Mathematical Models for Single-Cell Data Analysis written by Le Ou-Yang and published by Frontiers Media SA. This book was released on 2022-11-29 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Medical Imaging

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Author : K.C. Santosh
Publisher : CRC Press
ISBN 13 : 0429642490
Total Pages : 251 pages
Book Rating : 4.4/5 (296 download)

Book Synopsis Medical Imaging by : K.C. Santosh

Download or read book Medical Imaging written by K.C. Santosh and published by CRC Press. This book was released on 2019-08-20 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. 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.

Deep Learning and Convolutional Neural Networks for Medical Image Computing

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Author : Le Lu
Publisher : Springer
ISBN 13 : 331942999X
Total Pages : 327 pages
Book Rating : 4.3/5 (194 download)

Book Synopsis Deep Learning and Convolutional Neural Networks for Medical Image Computing by : Le Lu

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.

Statistical and Modelling Techniques to Build Confidence in the Investigation of Immunology Through Agent-based Simulation

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Author :
Publisher :
ISBN 13 :
Total Pages : 316 pages
Book Rating : 4.:/5 (931 download)

Book Synopsis Statistical and Modelling Techniques to Build Confidence in the Investigation of Immunology Through Agent-based Simulation by :

Download or read book Statistical and Modelling Techniques to Build Confidence in the Investigation of Immunology Through Agent-based Simulation written by and published by . This book was released on 2011 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: