Deep Learning for Medical Image Analysis
by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
2nd Edition
Publisher: Academic Press
Book Details
| Print ISBN | 9780323851244 |
| eText ISBN | 9780323858885 |
| Publisher | Academic Press |
| Publishing Year | 2024 |
| Edition | 2nd Edition |
| Language | English |
| Pages | 518 |
Deep Learning for Medical Image Analysis, 2nd Edition provides comprehensive reference coverage of computational deep learning models applied to medical image analysis. Edited by S. Kevin Zhou, Hayit Greenspan, and Dinggang Shen, this publication details specialized methodologies for image detection, structural segmentation, registration, and computer-aided medical diagnostic tools.
The reference synthesizes advanced algorithmic techniques with concrete clinical target domains. Methodological chapters explain deep reinforcement learning, capsule networks, Transformer models, hypergraph learning, and generative adversarial networks. Practical implementation chapters demonstrate these computational frameworks within multi-modality cardiac image analysis, functional brain mapping, and video frame analysis for polyp detection during colonoscopy procedures.
As part of The MICCAI Society Book Series published by Academic Press, this volume provides organized reference material for specialized technical research. The content fits academic researchers, industry engineering teams, practicing clinicians, and radiographers who evaluate and deploy computational image processing models within clinical settings.
Table of Contents
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 images
Chapter 6: Hypergraph 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
Chapter 10: Dynamic inference using neural architecture search in medical image segmentation
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, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning
Chapter 16: OCTA Segmentation with limited training data using disentangled represenatation learning
Chapter 17: Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging
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