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
Auflage
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Illustrationen
165 illustrations (135 in full color)
Dateigröße
ISBN-13
978-0-323-85888-5 (9780323858885)
Schweitzer Klassifikation
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
