Deep Learning for Medical Image Analysis
2nd Edition
Published on 27. November 2023
Book
Paperback/Softback
518 pages
978-0-323-85124-4 (ISBN)
Description
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.
More details
Series
Edition
2nd edition
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
College/higher education
Illustrations
165 illustrations (135 in full color); Illustrations
Dimensions
Height: 237 mm
Width: 193 mm
Thickness: 32 mm
Weight
1133 gr
ISBN-13
978-0-323-85124-4 (9780323851244)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
S. Kevin Zhou | Hayit Greenspan | Dinggang Shen
Deep Learning for Medical Image Analysis
E-Book
11/2023
2nd Edition
Academic Press
€113.00
Available for download
Previous edition
S. Kevin Zhou | Hayit Greenspan | Dinggang Shen
Deep Learning for Medical Image Analysis
Book
01/2017
Academic Press
€121.31
Shipment within 15-20 days
Persons
S. Kevin Zhou, PhD is dedicated to research on medical image computing, especially analysis and reconstruction, and its applications in real practices. Currently, he is a Distinguished Professor and Founding Executive Dean of School of Biomedical Engineering, University of Science and Technology of China (USTC) and directs the Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE). Dr. Zhou was a Principal Expert and a Senior R&D Director at Siemens Healthcare Research. He has been elected as a fellow of AIMBE, IAMBE, IEEE, MICCAI and NAI and serves the MICCAI society as a board member and treasurer.. Hayit Greenspan, PhD is focused on developing deep learning tools for medical image analysis, as well as their translation to the clinic. She is a Professor of Biomedical Engineering with the Faculty of Engineering at Tel-Aviv University (on Leave), and currently with the Department of Radiology and the AI and Human Health Department at the Icahn School of Medicine at Mount Sinai, NYC. She is the Director of the AI Core at the Biomedical Engineering and Imaging (BMEII) Institute and the Co-director of a new AI and emerging technologies PhD program at Mount Sinai. Dr. Greenspan is also a co-founder of RADLogics Inc., a startup company bringing AI tools to clinician support Dinggang Shen, PhD is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, AIMBE, IAPR and MICCAI. He was a Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with the University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA. His research interests include medical image analysis, computer vision and pattern recognition. He has published more than 1,500 peer-reviewed papers in the international journals and conference proceedings, with H-index 130 and over 70K citations.
Editor
Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA
Head, Medical Image Processing and Analysis Lab, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, Israel
Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA
Content
1. An Introduction to Neural Networks and Deep Learning
2. Deep reinforcement learning in medical imaging
3. CapsNet for medical image segmentation
4.Transformer for Medical Image Analysis
5. An overview of disentangled representation learning for MR images
6. Hypergraph Learning and Its Applications for Medical Image Analysis
7. Unsupervised Domain Adaptation for Medical Image Analysis
8. Medical image synthesis and reconstruction using generative adversarial networks
9. Deep Learning for Medical Image Reconstruction
10. Dynamic inference using neural architecture search in medical image segmentation
11. Multi-modality cardiac image analysis with deep learning
12. Deep Learning-based Medical Image Registration
13. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI
14. Deep Learning in Functional Brain Mapping and associated applications
15. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning
16. OCTA Segmentation with limited training data using disentangled represenatation learning
17. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging
2. Deep reinforcement learning in medical imaging
3. CapsNet for medical image segmentation
4.Transformer for Medical Image Analysis
5. An overview of disentangled representation learning for MR images
6. Hypergraph Learning and Its Applications for Medical Image Analysis
7. Unsupervised Domain Adaptation for Medical Image Analysis
8. Medical image synthesis and reconstruction using generative adversarial networks
9. Deep Learning for Medical Image Reconstruction
10. Dynamic inference using neural architecture search in medical image segmentation
11. Multi-modality cardiac image analysis with deep learning
12. Deep Learning-based Medical Image Registration
13. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI
14. Deep Learning in Functional Brain Mapping and associated applications
15. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning
16. OCTA Segmentation with limited training data using disentangled represenatation learning
17. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging