Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Artificial Intelligence Deep Learning In Oncological Radiomics And Challenges Of Interpretability And Data Harmonization
Download Artificial Intelligence Deep Learning In Oncological Radiomics And Challenges Of Interpretability And Data Harmonization full books in PDF, epub, and Kindle. Read online Artificial Intelligence Deep Learning In Oncological Radiomics And Challenges Of Interpretability And Data Harmonization ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Artificial Intelligence: Deep Learning in Oncological Radiomics and Challenges of Interpretability and Data Harmonization by : Dani Wade
Download or read book Artificial Intelligence: Deep Learning in Oncological Radiomics and Challenges of Interpretability and Data Harmonization written by Dani Wade and published by . This book was released on 2021-04-09 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI in oncologyHealthcare is expected to be highly impacted by machine learning (ML)-based artificial intelligence (AI). As deep learning (DL) relying on neural networks trained with large datasets has demonstrated state-of- the-art performances in numerous applications, massive structural changes in information and data processing in this sector are expected. Oncology is especially targeted by these developments, cancer being a major worldwide issue (18.1 million cases and 9.6 million deaths in 2018, respectively 22 and 13 million projected for 2030) [1]. Regarding predictive modeling based on multimodal medical imaging such as CT (computed tomography), PET/CT (positron emission tomography / CT) or MRI (magnetic resonance imaging), both academic and private research rely on ML/DL methods, however their clinical implementation and acceptability are currently lacking.
Book Synopsis Machine Learning With Radiation Oncology Big Data by : Jun Deng
Download or read book Machine Learning With Radiation Oncology Big Data written by Jun Deng and published by Frontiers Media SA. This book was released on 2019-01-21 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Explainable and Transparent AI and Multi-Agent Systems by : Davide Calvaresi
Download or read book Explainable and Transparent AI and Multi-Agent Systems written by Davide Calvaresi and published by Springer Nature. This book was released on 2022-09-22 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Workshop on Explainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2022, held virtually during May 9–10, 2022. The 14 full papers included in this book were carefully reviewed and selected from 25 submissions. They were organized in topical sections as follows: explainable machine learning; explainable neuro-symbolic AI; explainable agents; XAI measures and metrics; and AI & law.
Book Synopsis Radiomics and Radiogenomics in Neuro-Oncology by : Sanjay Saxena
Download or read book Radiomics and Radiogenomics in Neuro-Oncology written by Sanjay Saxena and published by Elsevier. This book was released on 2024-04-08 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuro-oncology broadly encompasses life-threatening brain and spinal cord malignancies, including primary lesions and lesions metastasizing to the central nervous system. It is well suited for diagnosis, classification, and prognosis as well as assessing treatment response. Radiomics and Radiogenomics (R-n-R) have become two central pillars in precision medicine for neuro-oncology.Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while Radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Due to the exponential progress of different computational algorithms, AI methods are composed to advance the precision of diagnostic and therapeutic approaches in neuro-oncology.The field of radiomics has been and definitely will remain at the lead of this emerging discipline due to its efficiency in the field of neuro-oncology. Several AI approaches applied to conventional and advanced medical imaging data from the perspective of radiomics are very efficient for tasks such as survival prediction, heterogeneity analysis of cancer, pseudo progression analysis, and infiltrating tumors. Radiogenomics advances our understanding and knowledge of cancer biology, letting noninvasive sampling of the molecular atmosphere with high spatial resolution along with a systems-level understanding of causal heterogeneous molecular and cellular processes. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility across large multi-institutional cohorts.Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm provides readers with a broad and detailed framework for R-n-R approaches with AI in neuro-oncology, the description of cancer biology and genomics study of cancer, and the methods usually implemented for analyzing. Readers will also learn about the current solutions R-n-R can offer for personalized treatments of patients, limitations, and prospects. There is comprehensive coverage of information based on radiomics, radiogenomics, cancer biology, and medical image analysis viewpoints on neuro-oncology, so this in-depth coverage is divided into two Volumes.Volume 1: Radiogenomics Flow Using Artificial Intelligence provides coverage of genomics and molecular study of brain cancer, medical imaging modalities and analysis in neuro-oncology, and prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications provides coverage of imaging signatures for brain cancer molecular characteristics, clinical applications of R-n-R in neuro-oncology, and Machine Learning and Deep Learning AI approaches for R-n-R in neuro-oncology. Includes coverage on the foundational concepts of the emerging fields of radiomics and radiogenomics Covers neural engineering modeling and AI algorithms for the imaging, diagnosis, and predictive modeling of neuro-oncology Presents crucial technologies and software platforms, along with advanced brain imaging techniques such as quantitative imaging using CT, PET, and MRI Provides in-depth technical coverage of computational modeling techniques and applied mathematics for brain tumor segmentation and radiomics features such as extraction and selection
Book Synopsis Machine and Deep Learning in Oncology, Medical Physics and Radiology by : Issam El Naqa
Download or read book Machine and Deep Learning in Oncology, Medical Physics and Radiology written by Issam El Naqa and published by Springer Nature. This book was released on 2022-02-02 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Book Synopsis Basics of Image Processing by : Ángel Alberich-Bayarri
Download or read book Basics of Image Processing written by Ángel Alberich-Bayarri and published by Springer Nature. This book was released on with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence In Radiation Oncology by : Seong K Mun
Download or read book Artificial Intelligence In Radiation Oncology written by Seong K Mun and published by World Scientific. This book was released on 2022-12-27 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology.
Book Synopsis Radiomics and Its Clinical Application by : Jie Tian
Download or read book Radiomics and Its Clinical Application written by Jie Tian and published by Academic Press. This book was released on 2021-06-03 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. An introduction to the concepts of radiomics In-depth presentation of the core technologies and methods Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms
Book Synopsis Novel Methods for Oncologic Imaging Analysis: Radiomics, Machine Learning, and Artificial Intelligence by : Xuelei Ma
Download or read book Novel Methods for Oncologic Imaging Analysis: Radiomics, Machine Learning, and Artificial Intelligence written by Xuelei Ma and published by Frontiers Media SA. This book was released on 2021-09-23 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence and MRI: Boosting Clinical Diagnosis by : Antonio Napolitano
Download or read book Artificial Intelligence and MRI: Boosting Clinical Diagnosis written by Antonio Napolitano and published by Frontiers Media SA. This book was released on 2022-08-05 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings of the 12th International Conference on Computer Engineering and Networks by : Qi Liu
Download or read book Proceedings of the 12th International Conference on Computer Engineering and Networks written by Qi Liu and published by Springer Nature. This book was released on 2022-10-19 with total page 1506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This conference proceeding is a collection of the papers accepted by the CENet2022 – the 12th International Conference on Computer Engineering and Networks held on November 4-7, 2022 in Haikou, China. The topics focus but are not limited to Internet of Things and Smart Systems, Artificial Intelligence and Applications, Communication System Detection, Analysis and Application, and Medical Engineering and Information Systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings. This will enable them to produce, maintain, and manage systems with high levels of trustworthiness and complexity.
Book Synopsis Introduction to Artificial Intelligence by : Michail E. Klontzas
Download or read book Introduction to Artificial Intelligence written by Michail E. Klontzas and published by Springer Nature. This book was released on 2023-09-15 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to provide physicians and scientists with the basics of Artificial Intelligence (AI) with a special focus on medical imaging. The contents of the book provide an introduction to the main topics of artificial intelligence currently applied on medical image analysis. The book starts with a chapter explaining the basic terms used in artificial intelligence for novice readers and embarks on a series of chapters each one of which provides the basics on one AI-related topic. The second chapter presents the programming languages and available automated tools that enable the development of AI applications for medical imaging. The third chapter endeavours to analyse the main traditional machine learning techniques, explaining algorithms such as random forests, support vector machines as well as basic neural networks. The applications of those machines on the analysis of radiomics data is expanded in the fourth chapter to allow the understanding of algorithms used to build classifiers for the diagnosis of disease processes with the use of radiomics. Chapter five provides the basics of natural language processing which has revolutionized the analysis of complex radiological reports and chapter six affords a succinct introduction to convolutional neural networks which have revolutionized medical image analysis enabling automated image-based diagnosis, image enhancement (e.g. denoising), protocolling etc. The penultimate chapter provides an introduction to data preprocessing for use in the aforementioned artificial intelligence applications. The book concludes with a chapter demonstrating AI-based tools already in radiological practice while providing an insight about the foreseeable future. It will be a valuable resource for radiologists, computer scientists and postgraduate students working on medical image analysis.
Book Synopsis Artificial Intelligence and Machine Learning for Digital Pathology by : Andreas Holzinger
Download or read book Artificial Intelligence and Machine Learning for Digital Pathology written by Andreas Holzinger and published by Springer Nature. This book was released on 2020-06-24 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.
Book Synopsis Machine Learning and Artificial Intelligence in Radiation Oncology by : Barry S. Rosenstein
Download or read book Machine Learning and Artificial Intelligence in Radiation Oncology written by Barry S. Rosenstein and published by Academic Press. This book was released on 2023-12-02 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic
Book Synopsis Artificial Intelligence in Medical Imaging by : Erik R. Ranschaert
Download or read book Artificial Intelligence in Medical Imaging written by Erik R. Ranschaert and published by Springer. This book was released on 2019-01-29 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Book Synopsis Artificial Intelligence in Radiation Oncology and Biomedical Physics by : Gilmer Valdes
Download or read book Artificial Intelligence in Radiation Oncology and Biomedical Physics written by Gilmer Valdes and published by CRC Press. This book was released on 2023-08-14 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics. AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided. This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession.
Book Synopsis Artificial Intelligence and Machine Learning in Public Healthcare by : KC Santosh
Download or read book Artificial Intelligence and Machine Learning in Public Healthcare written by KC Santosh and published by Springer Nature. This book was released on 2022-01-01 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example—a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.
