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Deep Learning for Medical Image Analysis,

Edition 2

Editors: Edited by S. Kevin Zhou, Hayit Greenspan and Dinggang Shen

Publication Date: 27 Nov 2023

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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.

Key Features

Covers common research problems in medical image analysis and their challenges
Describes the latest 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 cardiac, neural and functional, colonoscopy, OCTA applications and model assessment· Includes a Foreword written by Nicholas Ayache

About the author

Edited by S. Kevin Zhou, Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA; Hayit Greenspan, Head, Medical Image Processing and Analysis Lab, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, Israel and Dinggang Shen, Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA

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

ISBN: 9780323851244

Page Count: 518

Illustrations : 165 illustrations (135 in full color)

Retail Price (USD) :

9780128040768

Academic and industry researchers and graduate students in medical imaging, computer vision, biomedical engineering, Clinicians, radiographers