TY - BOOK ID - 48259944 TI - Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics AU - Lu, Le. AU - Wang, Xiaosong. AU - Carneiro, Gustavo. AU - Yang, Lin. PY - 2019 SN - 3030139697 3030139689 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computer vision. KW - Radiology, Medical. KW - Artificial intelligence. KW - Image Processing and Computer Vision. KW - Imaging / Radiology. KW - Artificial Intelligence. KW - Mathematical Models of Cognitive Processes and Neural Networks. KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Clinical radiology KW - Radiology, Medical KW - Radiology (Medicine) KW - Medical physics KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Image processing KW - Pattern recognition systems KW - Machine learning. KW - Learning, Machine KW - Optical data processing. KW - Radiology. KW - Neural networks (Computer science) . KW - Artificial neural networks KW - Nets, Neural (Computer science) KW - Networks, Neural (Computer science) KW - Neural nets (Computer science) KW - Natural computation KW - Soft computing KW - Radiological physics KW - Physics KW - Radiation KW - Optical computing KW - Visual data processing KW - Integrated optics KW - Photonics KW - Computers KW - Optical equipment UR - https://www.unicat.be/uniCat?func=search&query=sysid:48259944 AB - This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation. ER -