Deep Learning for Medical Image Analysis
Series: The MICCAI Society book Series;
- Publisher's listprice EUR 113.00
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47 934 Ft (45 652 Ft + 5% VAT)
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47 934 Ft
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Product details:
- Edition number 2
- Publisher Academic Press
- Date of Publication 27 November 2023
- ISBN 9780323851244
- Binding Paperback
- No. of pages518 pages
- Size 234x190 mm
- Weight 1050 g
- Language English
- Illustrations 165 illustrations (135 in full color) 557
Categories
Long description:
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.
MoreTable of Contents:
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
Deep Learning for Medical Image Analysis
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