Cantitate/Preț
Produs
Total produse
Vezi coșul
Deep Learning for Medical Image Analysis de S. Kevin Zhou

Deep Learning for Medical Image Analysis: The MICCAI Society book Series

Editat de S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
en Limba Engleză Paperback – 27 noi 2023
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
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (2) 53754 lei  5-7 săpt.
  ELSEVIER SCIENCE – 30 ian 2017 53754 lei  5-7 săpt.
  ELSEVIER SCIENCE – 27 noi 2023 55416 lei  5-7 săpt. +14841 lei  6-12 zile

Din seria The MICCAI Society book Series

Preț: 55416 lei

Preț vechi: 82009 lei
-32% Nou

Puncte Express: 831
Preț estimativ în valută:
10606€ • 11189$ • 8838£

Carte tipărită la comandă

Livrare economică 26 decembrie 24 - 09 ianuarie 25
Livrare express 27 noiembrie-03 decembrie pentru 15840 lei

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9780323851244
ISBN-10: 032385124X
Pagini: 518
Ilustrații: 165 illustrations (135 in full color)
Dimensiuni: 191 x 235 x 30 mm
Greutate: 1.13 kg
Ediția:2
Editura: ELSEVIER SCIENCE
Seria The MICCAI Society book Series

Cuprins

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

Descriere

Descriere de la o altă ediție sau format:

Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.
Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
 

  • Covers common research problems in medical image analysis and their challenges
  • Describes deep learning methods and the theories behind approaches for medical image analysis
  • Teaches how algorithms are applied to a broad range of application areas, includingChest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.

"