Deep Learning for Medical Image Analysis

by S. Kevin Zhou (Editor), Hayit Greenspan (Editor), Dinggang Shen (Editor)

Overview

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 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

Product Details

ISBN-13:	9780323858885
Publisher:	Elsevier Science & Technology Books
Publication date:	11/23/2023
Series:	The MICCAI Society book Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	600
File size:	64 MB
Note:	This product may take a few minutes to download.

About the Author

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.

Table of Contents

1. An Introduction to Neural Networks and Deep Learning2. Deep reinforcement learning in medical imaging3. CapsNet for medical image segmentation4.Transformer for Medical Image Analysis5. An overview of disentangled representation learning for MR images6. Hypergraph Learning and Its Applications for Medical Image Analysis7. Unsupervised Domain Adaptation for Medical Image Analysis8. Medical image synthesis and reconstruction using generative adversarial networks9. Deep Learning for Medical Image Reconstruction10. Dynamic inference using neural architecture search in medical image segmentation11. Multi-modality cardiac image analysis with deep learning12. Deep Learning-based Medical Image Registration13. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI14. Deep Learning in Functional Brain Mapping and associated applications15. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning16. OCTA Segmentation with limited training data using disentangled represenatation learning17. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging

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From the Publisher

Applies deep learning methods to medical imaging, providing a clear understanding of the principles and methods of neural network and deep learning