Deep Learning and Convolutional Neural Networks for Medical Image Computing | Precision Medicine, High Performance and Large-Scale Datasets | ISBN 9783319827131

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Precision Medicine, High Performance and Large-Scale Datasets

herausgegeben von Le Lu, Yefeng Zheng, Gustavo Carneiro und Lin Yang
Mitwirkende
Herausgegeben vonLe Lu
Herausgegeben vonYefeng Zheng
Herausgegeben vonGustavo Carneiro
Herausgegeben vonLin Yang
Buchcover Deep Learning and Convolutional Neural Networks for Medical Image Computing  | EAN 9783319827131 | ISBN 3-319-82713-8 | ISBN 978-3-319-82713-1

“This book … is very suitable for students, researchers and practitioner. In addition, the book provides an important and useful reference for experienced researchers on particular aspects of deep learning based medical image analysis.” (Guang Yang, IAPR Newsletter, Vol. 41 (2), April, 2019)

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Precision Medicine, High Performance and Large-Scale Datasets

herausgegeben von Le Lu, Yefeng Zheng, Gustavo Carneiro und Lin Yang
Mitwirkende
Herausgegeben vonLe Lu
Herausgegeben vonYefeng Zheng
Herausgegeben vonGustavo Carneiro
Herausgegeben vonLin Yang

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.