A new book in an open and hot area in machine learning, “Deep Learning and Convolutional Neural Network for Medical Image Computing” has been co-edited by University of Florida Biomedical Engineering Associate professor, Dr. Lin Yang. The link of the new book can be found in the following link: http://www.springer.com/us/book/9783319429984
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, microscopic image analysis, 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. This book 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.
Dr. Yang is the founder of the Biomedical Image Computing and Imaging Informatics (BICI2) lab. His major research interests are focus on biomedical image analysis and imaging informatics, computer vision, biomedical informatics, and machine learning. Currently, he is actively working on high-performance computing and computed-aided health care and information technology using deep learning for personalized/precise medicine. He has already published over 100 peer-reviewed articles and supported by multiple NIH R01 grants.
Dr. Le Lu, staff scientist, Radiology and Imaging Sciences Department, National Institutes of Health Clinical Center, Bethesda, MD
Dr. Yefeng Zheng, senior staff scientist, Siemens Healthcare Technology Center, Princeton, NJ
Dr. Gustavo Carneiro, associate professor, School of Computer Science, University of Adelaide, Australia
Congratulations, Dr. Yang!