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 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.

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)

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.

Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

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.

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.

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.

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.

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