Learning Diagnostic Imaging
Learning Diagnostic Imaging

Author: Ramón Ribes

Publisher: Springer Science & Business Media

Published: 2008-11-06

Total Pages: 259

ISBN-13: 3540712070

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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
Machine Learning and Medical Imaging

Author: Guorong Wu

Publisher: Academic Press

Published: 2016-08-11

Total Pages: 514

ISBN-13: 0128041145

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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

Medical Imaging
Medical Imaging

Author: K.C. Santosh

Publisher: CRC Press

Published: 2019-08-20

Total Pages: 200

ISBN-13: 0429639325

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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.

Deep Learning Models for Medical Imaging
Deep Learning Models for Medical Imaging

Author: KC Santosh

Publisher: Academic Press

Published: 2021-09-07

Total Pages: 172

ISBN-13: 0128236507

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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)

Core Radiology
Core Radiology

Author: Ellen X. Sun

Publisher: Cambridge University Press

Published: 2021-09-30

Total Pages: 1270

ISBN-13: 1108967868

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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.

Learning Radiology
Learning Radiology

Author: William Herring

Publisher: Saunders

Published: 2015-04-16

Total Pages: 0

ISBN-13: 9780323328074

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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.

Machine Learning in Medical Imaging
Machine Learning in Medical Imaging

Author: Chunfeng Lian

Publisher: Springer Nature

Published: 2021-09-25

Total Pages: 723

ISBN-13: 303087589X

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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 Medical Imaging
Machine Learning in Medical Imaging

Author: Mingxia Liu

Publisher: Springer Nature

Published: 2020-10-02

Total Pages: 702

ISBN-13: 3030598616

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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.

Learning Ultrasound Imaging
Learning Ultrasound Imaging

Author: Jose Luís del Cura

Publisher: Springer Science & Business Media

Published: 2012-10-26

Total Pages: 253

ISBN-13: 3642305857

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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.

Introduction to Diagnostic Radiology
Introduction to Diagnostic Radiology

Author: Khaled Elsayes

Publisher: McGraw Hill Professional

Published: 2014-11-22

Total Pages: 702

ISBN-13: 0071802312

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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.