Deep learning for medical image analysis / / edited by Kevin Zhou, Hayit Greenspan, Dinggang Shen

• Autore: Zhou S.
• Pubblicazione: London, England : , : Academic Press, , 2017; ©2017
• Edizione: First edition.
• Descrizione fisica: 1 online resource (460 pages) : illustrations, photographs
• Disciplina: 616.07540285
• Soggetto topico: Diagnostic imaging - Data processing; Image analysis
• Persona (resp. second.): ZhouS. Kevin; GreenspanHayit; ShenDinggang
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Introduction -- Medical Image Detection and recognition -- Medical image segmentation -- Medical image registration -- Computer-aided diagnosis and disease quantification -- Others.
• Sommario/riassunto: Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache
• Titolo autorizzato: Deep learning for medical image analysis
• ISBN: 0-12-810409-0; 0-12-810408-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910162740003321
• Lo trovi qui: Univ. Federico II