Deep Learning for Medical Image Analysis

S. Kevin Zhou, Hayit Greenspan, Dinggang Shen

• Publisher: Academic Press
• Year: 2023
• eBook ISBN: 9780323858885
• Edition: 2
• Topic: Computer Science
• Subtopic: Business Intelligence
• Index: Computer Science

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

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contents
• Contributors
• Foreword
• List of Illustrations
• List of Tables
• Chapter 1 : An introduction to neural networks and deep learning
• Chapter 2 : Deep reinforcement learning in medical imaging
• Chapter 3 : CapsNet for medical image segmentation
• Chapter 4 : Transformer for medical image analysis
• Chapter 5 : An overview of disentangled representation learning for MR image harmonization
• Chapter 6 : Hyper-graph learning and its applications for medical image analysis
• Chapter 7 : Unsupervised domain adaptation for medical image analysis
• Chapter 8 : Medical image synthesis and reconstruction using generative adversarial networks
• Chapter 9 : Deep learning for medical image reconstruction: Focus on MRI, CT and PET
• Chapter 10 : Dynamic inference using neural architecture search in medical image segmentation: From a novel adaptation perspective
• Chapter 11 : Multi-modality cardiac image analysis with deep learning
• Chapter 12 : Deep learning-based medical image registration
• Chapter 13 : Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI
• Chapter 14 : Deep learning in functional brain mapping and associated applications
• Chapter 15 : Detecting, localizing and classifying polyps from colonoscopy videos using deep learning
• Chapter 16 : OCTA segmentation with limited training data using disentangled representation learning
• Chapter 17 : Considerations in the assessment of machine learning algorithm performance for medical imaging
• Index
• 0–9