The use of deep learning in mapping and diagnosis of cancers
| Author | : Fu Wang |
| Publisher | : Frontiers Media SA |
| Total Pages | : 228 |
| Release | : 2023-01-18 |
| Genre | : Medical |
| ISBN | : 2832511694 |
The Use Of Deep Learning In Mapping And Diagnosis Of Cancers
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Deep Learning for Cancer Diagnosis
| Author | : Utku Kose |
| Publisher | : Springer Nature |
| Total Pages | : 311 |
| Release | : 2020-09-12 |
| Genre | : Technology & Engineering |
| ISBN | : 9811563217 |
This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
Machine Learning in Radiation Oncology
| Author | : Issam El Naqa |
| Publisher | : Springer |
| Total Pages | : 336 |
| Release | : 2015-06-19 |
| Genre | : Medical |
| ISBN | : 3319183052 |
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Machine Learning with Health Care Perspective
| Author | : Vishal Jain |
| Publisher | : Springer Nature |
| Total Pages | : 418 |
| Release | : 2020-03-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3030408507 |
This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.
Deep Learning Applications in Medical Imaging
| Author | : Saxena, Sanjay |
| Publisher | : IGI Global |
| Total Pages | : 274 |
| Release | : 2020-10-16 |
| Genre | : Medical |
| ISBN | : 1799850722 |
Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.
Deep Learning in Medical Image Analysis
| Author | : Gobert Lee |
| Publisher | : Springer Nature |
| Total Pages | : 184 |
| Release | : 2020-02-06 |
| Genre | : Medical |
| ISBN | : 3030331288 |
This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.
Approaches and Applications of Deep Learning in Virtual Medical Care
| Author | : Zaman, Noor |
| Publisher | : IGI Global |
| Total Pages | : 293 |
| Release | : 2022-02-25 |
| Genre | : Computers |
| ISBN | : 1799889300 |
The recent advancements in the machine learning paradigm have various applications, specifically in the field of medical data analysis. Research has proven the high accuracy of deep learning algorithms, and they have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases. Approaches and Applications of Deep Learning in Virtual Medical Care considers the applications of deep learning in virtual medical care and delves into complex deep learning algorithms, calibrates models, and improves the predictions of the trained model on medical imaging. Covering topics such as big data and medical sensors, this critical reference source is ideal for researchers, academicians, practitioners, industry professionals, hospital workers, scholars, instructors, and students.
Liquid Biopsy
| Author | : Ilze Strumfa |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 160 |
| Release | : 2019-07-10 |
| Genre | : Medical |
| ISBN | : 183881129X |
Reliable diagnosis is the cornerstone, starting point, and prerequisite of successful treatment. Therefore, development of innovative diagnostic technologies represents a hot topic in medical research. Liquid biopsy is a novel, minimally invasive laboratory evaluation concept for diagnostic, prognostic, and predictive testing, as well as dynamic monitoring of treatment or disease course. To achieve these goals, a multitude of specific, targeted tests can be performed to detect free nucleic acids, exosomes, microRNAs, tumor-educated platelets, and whole cells of tumor or fetal origin in different biological fluids, including blood, urine, cerebrospinal fluid, and others. Although tissue biopsy has long been considered the gold standard of diagnostics, especially regarding malignant tumors, liquid biopsy has the advantages of a non-invasive approach and thus low risk of complications. It is technically feasible even in serious general status or if tumors or metastases are not easily accessible using conventional tissue biopsy. The testing is fast, exact, and can be repeated to ensure real-time follow-up. In contrast to classic tumor markers, liquid biopsy is distinguished by high specificity at genomic, proteomic, and cellular levels. It is expected to equal and exceed the diagnostic value of tissue biopsy. The field of liquid biopsies is developing rapidly regarding the selection of targets, technological improvements, and quality assessment. This book, written by a global team of recognized scientists, comprises state-of-the-art reviews on the current knowledge and advances in the technologies and software for liquid biopsy. Examples of practical application of liquid biopsy to evaluate thyroid cancer, multiple myeloma, etc. are discussed as well. The book is intended to serve as a reference for scientists and clinicians interested in the development and practical implementation of liquid biopsy.
Machine Learning for Healthcare Applications
| Author | : Sachi Nandan Mohanty |
| Publisher | : John Wiley & Sons |
| Total Pages | : 418 |
| Release | : 2021-04-13 |
| Genre | : Computers |
| ISBN | : 1119791812 |
When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.
Multimodal Scene Understanding
| Author | : Michael Ying Yang |
| Publisher | : Academic Press |
| Total Pages | : 424 |
| Release | : 2019-07-16 |
| Genre | : Technology & Engineering |
| ISBN | : 0128173599 |
Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning
