Artificial Intelligence in Cancer Book

Artificial Intelligence in Cancer


  • Author : Smaranda Belciug
  • Publisher : Academic Press
  • Release Date : 2020-07-15
  • Genre: Science
  • Pages : 318
  • ISBN 10 : 9780128202012

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Artificial Intelligence in Cancer Excerpt :

Artificial Intelligence in Cancer: Diagnostic to Tailored Treatment provides theoretical concepts and practical techniques of AI and its applications in cancer management, building a roadmap on how to use AI in cancer at different stages of healthcare. It discusses topics such as the impactful role of AI during diagnosis and how it can support clinicians to make better decisions, AI tools to help pathologists identify exact types of cancer, how AI supports tumor profiling and can assist surgeons, and the gains in precision for oncologists using AI tools. Additionally, it provides information on AI used for survival and remission/recurrence analysis. The book is a valuable source for bioinformaticians, cancer researchers, oncologists, clinicians and members of the biomedical field who want to understand the promising field of AI applications in cancer management. Discusses over 20 real cancer examples, bringing state-of-the-art cancer cases in which AI was used to help the medical personnel Presents over 100 diagrams, making it easier to comprehend AI's results on a specific problem through visual resources Explains AI algorithms in a friendly manner, thus helping the reader implement or use them in a specific cancer case

Artificial Intelligence in Breast Cancer Early Detection and Diagnosis Book

Artificial Intelligence in Breast Cancer Early Detection and Diagnosis


  • Author : Khalid Shaikh
  • Publisher : Springer Nature
  • Release Date : 2020-12-04
  • Genre: Technology & Engineering
  • Pages : 107
  • ISBN 10 : 9783030592080

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Artificial Intelligence in Breast Cancer Early Detection and Diagnosis Excerpt :

This book provides an introduction to next generation smart screening technology for medical image analysis that combines artificial intelligence (AI) techniques with digital screening to develop innovative methods for detecting breast cancer. The authors begin with a discussion of breast cancer, its characteristics and symptoms, and the importance of early screening.They then provide insight on the role of artificial intelligence in global healthcare, screening methods for breast cancer using mammogram, ultrasound, and thermogram images, and the potential benefits of using AI-based systems for clinical screening to more accurately detect, diagnose, and treat breast cancer. Discusses various existing screening methods for breast cancer Presents deep information on artificial intelligence-based screening methods Discusses cancer treatment based on geographical differences and cultural characteristics

Artificial Intelligence in Oncology Drug Discovery and Development Book

Artificial Intelligence in Oncology Drug Discovery and Development


  • Author : John Cassidy
  • Publisher : BoD – Books on Demand
  • Release Date : 2020-09-09
  • Genre: Medical
  • Pages : 192
  • ISBN 10 : 9781789846898

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Artificial Intelligence in Oncology Drug Discovery and Development Excerpt :

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence.

Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis Book

Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis


  • Author : Ashlesha Jain
  • Publisher : World Scientific
  • Release Date : 2000
  • Genre: Computers
  • Pages : 330
  • ISBN 10 : 9789810243746

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Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis Excerpt :

The main aim of this book is to present a sample of recent research on the application of novel artificial intelligence paradigms to the diagnosis and prognosis of breast cancer. These paradigms include neural networks, fuzzy logic and evolutionary computing. Artificial intelligence techniques offer advantages ? such as adaptation, fault tolerance, learning and human-like behavior ? over conventional computing techniques. The idea is to combine the pathological, intelligent and statistical approaches to enable simple and accurate diagnosis and prognosis.This book is the first of its kind on the topic of artificial intelligence in breast cancer. It presents the applications of artificial intelligence in breast cancer diagnosis and prognosis, and includes state-of-the-art concepts in the field. It contains contributions from Australia, Germany, Italy, UK and the USA.

Deep Learning for Cancer Diagnosis Book

Deep Learning for Cancer Diagnosis


  • Author : Utku Kose
  • Publisher : Springer Nature
  • Release Date : 2020-09-12
  • Genre: Technology & Engineering
  • Pages : 300
  • ISBN 10 : 9789811563218

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Deep Learning for Cancer Diagnosis Excerpt :

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.

Automation  Control and Energy Efficiency in Complex Systems Book

Automation Control and Energy Efficiency in Complex Systems


  • Author : Hamid Khayyam
  • Publisher : MDPI
  • Release Date : 2020-12-22
  • Genre: Technology & Engineering
  • Pages : 242
  • ISBN 10 : 9783039436279

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Automation Control and Energy Efficiency in Complex Systems Excerpt :

This book is aimed at serving researchers, engineers, scientists, and engineering graduate and PhD students of engineering and physical science together with individuals interested in engineering and science. This book focuses on the application of engineering methods to complex systems including transportation, building, and manufacturing, with approaches representing a wide variety of disciplines of engineering and science. Throughout the book, great emphases are placed on engineering applications of complex systems, as well as the methodologies of automation, including artificial intelligence, automated and intelligent control, energy analysis, energy modelling, energy management, and optimised energy efficiency. The significant impact of recent studies that have been selected for presentation are of high interest in engineering complex systems. An attempt has been made to expose the reading audience of engineers and researchers to a broad range of theoretical and practical topics. The topics contained in the present book are of specific interest to engineers who are seeking expertise in transportation, building, and manufacturing technologies as well as mathematical modelling of complex systems, engineering approaches to engineering complex problems, automation via artificial intelligence methods, automated and intelligent control, and energy systems. The primary audience of this book are researchers, graduate students, and engineers in mechanical engineering, control engineering, computer engineering, electrical engineering, and science disciplines. In particular, the book can be used for training graduate and PhD students as well as senior undergraduate students to enhance their knowledge by taking a graduate or advanced undergraduate course in the areas of complex systems, control systems, energy systems, and engineering applications. The covered research topics are also of interest to engineers and academia who are seeking to expand their expertise in these

Artificial Intelligence and Deep Learning in Pathology Book

Artificial Intelligence and Deep Learning in Pathology


  • Author : Stanley Cohen
  • Publisher : Elsevier Health Sciences
  • Release Date : 2020-06-02
  • Genre: Medical
  • Pages : 288
  • ISBN 10 : 9780323675376

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Artificial Intelligence and Deep Learning in Pathology Excerpt :

Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning Book

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning


  • Author : Rani, Geeta
  • Publisher : IGI Global
  • Release Date : 2020-10-16
  • Genre: Medical
  • Pages : 586
  • ISBN 10 : 9781799827436

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Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning Excerpt :

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Machine Learning in Radiation Oncology Book

Machine Learning in Radiation Oncology


  • Author : Issam El Naqa
  • Publisher : Springer
  • Release Date : 2015-06-19
  • Genre: Medical
  • Pages : 336
  • ISBN 10 : 9783319183053

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Machine Learning in Radiation Oncology Excerpt :

​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.

Big Data in Radiation Oncology Book

Big Data in Radiation Oncology


  • Author : Jun Deng
  • Publisher : CRC Press
  • Release Date : 2019-03-07
  • Genre: Science
  • Pages : 289
  • ISBN 10 : 9781351801126

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Big Data in Radiation Oncology Excerpt :

Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medica

Big Data and Artificial Intelligence for Healthcare Applications Book

Big Data and Artificial Intelligence for Healthcare Applications


  • Author : Ankur Saxena
  • Publisher : CRC Press
  • Release Date : 2021-06-15
  • Genre: Computers
  • Pages : 286
  • ISBN 10 : 9781000387315

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Big Data and Artificial Intelligence for Healthcare Applications Excerpt :

This book covers a wide range of topics on the role of Artificial Intelligence, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. This book explores the applications in different areas of healthcare and highlights the current research. "Big Data and Artificial Intelligence for Healthcare Applications" covers healthcare big data analytics, mobile health and personalized medicine, clinical trial data management and presents how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis. It also offers some case studies that describes the application of Artificial Intelligence and Machine Learning in healthcare. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

Artificial Intelligence for Data Driven Medical Diagnosis Book

Artificial Intelligence for Data Driven Medical Diagnosis


  • Author : Deepak Gupta
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release Date : 2021-02-08
  • Genre: Computers
  • Pages : 326
  • ISBN 10 : 9783110668322

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Artificial Intelligence for Data Driven Medical Diagnosis Excerpt :

This book collects research works of data-driven medical diagnosis done via Artificial Intelligence based solutions, such as Machine Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and healthcare. Medical data from different sources can also be analyzed via Artificial Intelligence techniques for more effective results.

Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Book

Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis


  • Author : Lu, Zhongyu
  • Publisher : IGI Global
  • Release Date : 2021-05-28
  • Genre: Medical
  • Pages : 263
  • ISBN 10 : 9781799873174

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Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Excerpt :

Cancer continues to be a growing problem as it is the foremost cause of death worldwide, killing millions of people each year. The number of people battling cancer continues to increase, owing to different reasons, such as lifestyle choices. Clinically, determining the cause of cancer is very challenging and often inaccurate. Incorporating efficient and accurate algorithms to detect cancer cases is becoming increasingly beneficial for scientists in computer science and healthcare, as well as a long-term benefit for doctors, patients, clinic practitioners, and more. Specifically, an automation of computation in machine learning could be a solution in the next generation of big data science technology. Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis presents algorithms that have been developed to evaluate big data approaches and cancer research. The chapters include artificial intelligence and machine learning approaches, as well as case studies to solve the predictive issues in colon cancer research. This book includes concepts and techniques used to run tasks in an automated manner with the intent to improve better accuracy in comparison with previous studies and methods. This book also covers the processes of research design, development, and outcome analytics in this field. Doctors, IT consultants, IT specialists, medical software professionals, data scientists, researchers, computer scientists, healthcare practitioners, academicians, and students can benefit from this critical resource.

Deep Learning for the Life Sciences Book

Deep Learning for the Life Sciences


  • Author : Bharath Ramsundar
  • Publisher : O'Reilly Media
  • Release Date : 2019-04-10
  • Genre: Science
  • Pages : 238
  • ISBN 10 : 9781492039808

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Deep Learning for the Life Sciences Excerpt :

Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working