- Häftad (Paperback)
- Antal sidor
- Academic Press
- Greenspan, Hayit / Shen, Dinggang
- Approx. 165 illustrations (135 in full color); Illustrations, unspecified
- Antal komponenter
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Deep Learning for Medical Image Analysisav S Kevin Zhou1261Ännu ej utkommen – klicka "Bevaka" för att få ett mejl så fort boken boken går att köpa.Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is 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 are applied to medical image detection, segmentation, registration, and computer-aided 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
Fler böcker av S Kevin Zhou
S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE). Hayit Greenspan is a Tenured Professor at the Biomedical Engineering Dept. Faculty of Engineering, Tel-Aviv University. She was a visiting Professor at the Radiology Dept. Stanford University, and is currently affiliated with the International Computer Science Institute (ICSI) at Berkeley. Dr. Greenspan's research focuses on image modeling and analysis, deep learning, and content-based image retrieval. Research projects include: Brain MRI research (structural and DTI), CT and X-ray image analysis - automated detection to segmentation and characterization. Dr. Greenspan has over 150 publications in leading international journals and conference proceedings. She has received several awards and is a coauthor on several patents. Currently her Lab is funded for Deep Learning in Medical Imaging by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI). Dr. Greenspan is a member of several journal and conference program committees, including SPIE medical imaging, IEEE_ISBI and MICCAI. She is an Associate Editor for the IEEE Trans on Medical Imaging (TMI) journal. Recently she was the Lead guest editor for an IEEE-TMI special Issue on "Deep Learning in Medical Imaging, May 2016. Dinggang Shen is a Professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). He is currently directing the Center for Image Informatics and Analysis, the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also the medical image analysis core in the BRIC. He was a tenure-track assistant professor in the University of Pennsylvanian (UPenn), and a faculty member in the Johns Hopkins University. Dr. Shen's research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 700 papers in the international journals and conference proceedings. He serves as an editorial board member for six international journals. He has served in the Board of Directors, The Medical Image Computing and C...
1. An Introduction to Neural Networks and Deep Learning 2. Medical Image Synthesis and Reconstruction 3. Dynamic Inference using Neural Architecture Search in Medical Image Segmentation 4. Cardiac 5. Applications of artificial intelligence in cardiovascular imaging 6. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning 7. An overview of disentangled representation learning for MR images 8. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging 9. Deep Learning for Medical Image Reconstruction 10. How to conduct a high quality clinical study (title TBC) 11. CapsNet 12. Hypergraph Learning and Its Applications for Medical Image Analysis 13. Unsupervised Domain Adaptation for Medical Image Analysis 14. Reinforcement Learning 15. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI 16. Deep Learning Models for Functional Brain Mapping 17. Medical Image Registration 18. Model Genesis 19. OCTA Segmentation 20. Transformer for Medical Image Analysis