Salvatore Claudio Fanni – författare

Methodology in Radiomics: Step-by-step Guide in Radiomics Pipeline is an essential resource for anyone interested in the rapidly evolving field of radiomics. This comprehensive guide delves into the history and fundamental principles of radiomics, providing readers with a clear understanding of how to implement each step in the radiomic pipeline. The book is designed to bridge the gap between radiomics research and clinical practice, making it an invaluable reference for imaging scientists, researchers, and medical professionals. The book not only covers the basics but also includes troubleshooting techniques to overcome common challenges.It discusses current research and future directions, offering insights into the potential of radiomics in clinical settings. This guide ensures that readers are well-equipped to navigate the complexities of radiomics, fostering a deeper understanding of its applications and advances in medical imaging.Provides a complete overview of the history and fundamentals of radiomicsIncludes step-by step guidance for designing a high-quality radiomic studyOffers comprehensive information necessary for the interpretation of radiomic studies’ methodology and resultsReviews radiomics applications in healthcare and future directions in research

Methodology in Radiomics: Step-by-step Guide in Radiomics Pipeline is an essential resource for anyone interested in the rapidly evolving field of radiomics. This comprehensive guide delves into the history and fundamental principles of radiomics, providing readers with a clear understanding of how to implement each step in the radiomic pipeline. The book is designed to bridge the gap between radiomics research and clinical practice, making it an invaluable reference for imaging scientists, researchers, and medical professionals. The book not only covers the basics but also includes troubleshooting techniques to overcome common challenges.It discusses current research and future directions, offering insights into the potential of radiomics in clinical settings. This guide ensures that readers are well-equipped to navigate the complexities of radiomics, fostering a deeper understanding of its applications and advances in medical imaging. - Provides a complete overview of the history and fundamentals of radiomics- Includes step-by step guidance for designing a high-quality radiomic study- Offers comprehensive information necessary for the interpretation of radiomic studies' methodology and results- Reviews radiomics applications in healthcare and future directions in research

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.