Download or read book Learning Diagnostic Imaging written by Ramón Ribes and published by Springer Science & Business Media. This book was released on 2008-11-06 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to diagnostic radiology (including nuclear medicine). Written in a user-friendly format, it takes into account that radiology is divided into many subspecialties that constitute a universe of their own. The book is subdivided into ten sections, such as musculoskeletal, thoracic, gastrointestinal, cardiovascular and breast imaging. Each chapter is presented with an introduction of the subspecialty and ten case studies with illustrations and comments.
Download or read book Machine Learning and Medical Imaging written by Guorong Wu and published by Academic Press. This book was released on 2016-08-11 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques
Download or read book Deep Learning Models for Medical Imaging written by KC Santosh and published by Academic Press. This book was released on 2021-09-07 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow ‘with’ and ‘without’ transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. Provides a step-by-step approach to develop deep learning models Presents case studies showing end-to-end implementation (source codes: available upon request)
Download or read book Learning Radiology written by William Herring and published by Saunders. This book was released on 2015-04-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A must-have for anyone who will be required to read and interpret common radiologic images, Learning Radiology: Recognizing the Basics is an image-filled, practical, and easy-to-read introduction to key imaging modalities. Skilled radiology teacher William Herring, MD, masterfully covers exactly what you need to know to effectively interpret medical images of all modalities. Learn the latest on ultrasound, MRI, CT, patient safety, dose reduction, radiation protection, and more, in a time-friendly format with brief, bulleted text and abundant high-quality images. Then ensure your mastery of the material with additional online content, bonus images, and self-assessment exercises at Student Consult.
Download or read book Learning Ultrasound Imaging written by Jose Luís del Cura and published by Springer Science & Business Media. This book was released on 2012-10-26 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a practical approach to the world of diagnostic ultrasound. It has been structured in a reader-friendly, case-based format that makes it easy and enjoyable to learn the basics of the applications and interpretation of ultrasound. Each case includes illustrations, descriptions of the imaging findings, and technical details and serves to identify the essential imaging features of the pathology under consideration, thus assisting the reader in the diagnosis of similar cases. The book is divided into 17 short chapters that review the most important areas of ultrasound application and also document the latest advances in the use of contrast and interventional ultrasound. The authors treat every topic from a “how to do it” perspective with the aim of imparting their wide experience in use of the technique. This book forms part of the Learning Imaging series for medical students, residents, less experienced radiologists, and other medical staff.
Download or read book Machine Learning in Bio Signal Analysis and Diagnostic Imaging written by Nilanjan Dey and published by Academic Press. This book was released on 2018-11-30 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains
Download or read book Core Radiology written by Ellen X. Sun and published by Cambridge University Press. This book was released on 2021-09-30 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embodying the principle of 'everything you need but still easy to read', this fully updated edition of Core Radiology is an indispensable aid for learning the fundamentals of radiology and preparing for the American Board of Radiology Core exam. Containing over 2,100 clinical radiological images with full explanatory captions and color-coded annotations, streamlined formatting ensures readers can follow discussion points effortlessly. Bullet pointed text concentrates on essential concepts, with text boxes, tables and over 400 color illustrations supporting readers' understanding of complex anatomic topics. Real-world examples are presented for the readers, encompassing the vast majority of entitles likely encountered in board exams and clinical practice. Divided into two volumes, this edition is more manageable whilst remaining comprehensive in its coverage of topics, including expanded pediatric cardiac surgery descriptions, updated brain tumor classifications, and non-invasive vascular imaging. Highly accessible and informative, this is the go-to introductory textbook for radiology residents worldwide.
Download or read book Introduction to Diagnostic Radiology written by Khaled Elsayes and published by McGraw Hill Professional. This book was released on 2014-11-22 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical clinically relevant introduction to diagnostic radiology Introduction to Basic Radiology is written to provide non-radiologists with the level of knowledge necessary to order correct radiological examinations, improve image interpretation, and enhance their interpretation of various radiological manifestations. The book focuses on the clinical scenarios most often encountered in daily practice and discusses practical imaging techniques and protocols used to address common problems. Relevant case scenarios are included to demonstrate how to reach a specific diagnosis. Introduction to Basic Radiology is divided into ten chapters. The first two chapters provide basic information on various diagnostic imaging techniques and control agents. Each of the following chapters discuss imaging of specific organ systems and begin with a description of the imaging modality of choice and illustrates the relevant features to help simplify the differential diagnosis. You will also find important chapters on pediatric radiology and women's imaging. Unlike other introductory texts on the subject, this book treats diagnosis from a practical point of view. Rather than discuss various diseases and classify them from the pathologic standpoint, Introduction to Basic Radiology utilizes cases from the emergency room and physician's offices and uses a practical approach to reach a diagnosis. The cases walk you through a radiology expert’s analysis of imaging patterns. These cases are presented progressively, with the expert's thinking process described in detail. The cases highlight clinical presentation, clinical suspicion, modality of choice, radiologic technique, and pertinent imaging features of common disease processes.
Download or read book The Practice of Radiology Education written by Teresa van Deven and published by Springer Science & Business Media. This book was released on 2009-10-13 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: The practice of radiology education: challenges and trends will provide truly helpful gu- ance for those of you involved in teaching and training in radiology. The goal of this book is ultimately to improve patient care. As a companion piece to the first book radiology education: the scholarship of teaching and learning, this book focuses on applying the concepts at a practical level that can be applied flexibly within educational programs for radiology residents and fellows in any medical imaging learning environment. This book focuses on the application of scholarship in terms of the “dissemination of useful, testable and reproducible information to others. ” It links educational theory with practice and for those of you who wish to explore educational practice further, a number of chapters s- gest additional readings and resources. The publication is timely and congruent with one of the most important twenty-first century trends in medical education: the move from amateurism to professionalism in teaching. In the past, medical schools and other health professions’ training institutions have been criticized for their resistance to the adoption of the science of medical edu- tion. Very few of us learned how to teach as medical students and most of us have our teaching responsibilities thrust on us with little preparation. The award of a basic medical degree was assumed to carry with it basic teaching expertise, unfortunately an unw- ranted assumption in some cases.
Download or read book Machine Learning in Medical Imaging written by Chunfeng Lian and published by Springer Nature. This book was released on 2021-09-25 with total page 723 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. *The workshop was held virtually.
Download or read book Learning Neuroimaging written by Francisco de Asís Bravo-Rodríguez and published by Springer Science & Business Media. This book was released on 2011-10-26 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended as an introduction to neuroradiology and aims to provide the reader with a comprehensive overview of this highly specialized radiological subspecialty. One hundred illustrated cases from clinical practice are presented in a standard way. Each case is supported by representative images and is divided into three parts: a brief summary of the patient’s medical history, a discussion of the disease, and a description of the most characteristic imaging features of the disorder. The focus is not only on common neuroradiological entities such as stroke and acute head trauma but also on less frequent disorders that the practitioner should recognize. Learning Neuroimaging: 100 Essential Cases is an ideal resource for neuroradiology and radiology residents, neurology residents, neurosurgery residents, nurses, radiology technicians, and medical students.
Download or read book Learning Interventional Radiology eBook written by Justin Shafa and published by Elsevier Health Sciences. This book was released on 2019-05-30 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Employs a case-based approach with a consistent chapter format to provide a clear, practical review of each topic. Each case-based chapter includes an Overview of the procedure and disease process, Indications and Contraindications of the procedure, standard Equipment used, a review of relevant Anatomy, detailed Procedural Steps, as well as Treatment Alternatives and common Complications. Reviews the skillful use of X-rays, CT, ultrasound, MRI, and other imaging methods to direct interventional procedures. Uses brief, bulleted text and more than 350 images to help you quickly grasp the fundamental information you need to know. Includes Take Home Points, Clinical Applications, Key Facts, Key Definitions, and Literature Reviews. Features case-based chapters on vascular and non-vascular procedures, as well as Grand Rounds Topics such as anatomy, surgery, interventional oncology, pediatrics, and more. Offers quick review and instruction for medical students, residents, fellows, and related medical professionals working in the IR area, such as nurse practitioners and physician assistants.
Download or read book Machine Learning in Medical Imaging written by Mingxia Liu and published by Springer Nature. This book was released on 2020-10-02 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
Download or read book Deep Learning in Medical Image Analysis written by Gobert Lee and published by Springer Nature. This book was released on 2020-02-06 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Medical Image Recognition Segmentation and Parsing written by S. Kevin Zhou and published by Academic Press. This book was released on 2015-12-11 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets Includes algorithms for recognizing and parsing of known anatomies for practical applications
Download or read book Deep Learning Applications in Medical Imaging written by Saxena, Sanjay and published by IGI Global. This book was released on 2020-10-16 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Learning Abdominal Imaging written by Antonio Luna and published by Springer Science & Business Media. This book was released on 2012-03-23 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an ideal introduction to the use of radiology in imaging diseases of the liver, gallbladder and biliary system, pancreas, spleen, and gastrointestinal tract. Each of the ten chapters is devoted to a particular organ and contains ten illustrated case reports drawn from clinical practice. Common clinical situations and indications for imaging are reviewed, and clear descriptions are provided of the various imaging techniques that will assist in resolving diagnostic and therapeutic dilemmas. This book is recommended for medical students, residents, and inexperienced abdominal radiologists.
