Deep Learning for Medical Image Analysis

Herausgeber: Zhou, S. Kevin; Shen, Dinggang; Greenspan, Hayit

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• The MICCAI Society book Series
• Verlag: Elsevier Science & Technology
• 2 ed
• Seitenzahl: 518
• Erscheinungstermin: 27. November 2023
• Englisch
• Abmessung: 237mm x 193mm x 32mm
• Gewicht: 1126g
• ISBN-13: 9780323851244
• ISBN-10: 032385124X
• Artikelnr.: 66902359

Inhaltsangabe

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