Learning Diagnostic Imaging

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.

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

This book covers the normal anatomy of the human body as seen in the entire gamut of medical imaging. It does so by an initial traditional anatomical description of each organ or system followed by the radiological anatomy of that part of the body using all the relevant imaging modalities. The third edition addresses the anatomy of new imaging techniques including three-dimensional CT, cardiac CT, and CT and MR angiography as well as the anatomy of therapeutic interventional radiological techniques guided by fluoroscopy, ultrasound, CT and MR. The text has been completely revised and over 140 new images, including some in colour, have been added. A series of ‘imaging pearls’ have been included with most sections to emphasise clinically and radiologically important points. The book is primarily aimed at those training in radiology and preparing for the FRCR examinations, but will be of use to all radiologists and radiographers both in training and in practice, and to medical students, physicians and surgeons and all who use imaging as a vital part of patient care. The third edition brings the basics of radiological anatomy to a new generation of radiologists in an ever-changing world of imaging. This book covers the normal anatomy of the human body as seen in the entire gamut of medical imaging. It does so by an initial traditional anatomical description of each organ or system followed by the radiological anatomy of that part of the body using all the relevant imaging modalities. The third edition addresses the anatomy of new imaging techniques including three-dimensional CT, cardiac CT, and CT and MR angiography as well as the anatomy of therapeutic interventional radiological techniques guided by fluoroscopy, ultrasound, CT and MR. The text has been completely revised and over 140 new images, including some in colour, have been added. A series of ‘imaging pearls’ have been included with most sections to emphasise clinically and radiologically important points. The book is primarily aimed at those training in radiology, but will be of use to all radiologists and radiographers both in training and in practice, and to medical students, physicians and surgeons and all who use imaging as a vital part of patient care. The third edition brings the basics of radiological anatomy to a new generation of radiologists in an ever-changing world of imaging. Anatomy of new radiological techniques and anatomy relevant to new staging or treatment regimens is emphasised. ‘Imaging Pearls’ that emphasise clinically and radiologically important points have been added throughout. The text has been revised to reflect advances in imaging since previous edition. Over 100 additional images have been added.

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.

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.

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.

Combines clinical images, full-color illustrations and bulleted text to create a comprehensive, up-to-date resource for learning and review.

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.