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商品コード: 9780128104088

Deep Learning for Medical Image Analysis

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書名

Deep Learning for Medical Image Analysis
著者・編者Zhou, S.K. et al.
出版社/発行元Academic Press
発行年/月2017年2月   
装丁Softcover
ページ数/巻数 625 ページ
ISBN 978-0-12-810408-8
発送予定海外倉庫よりお取り寄せ 2-3週間以内に発送します

Desciption

 

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.

Features

- 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, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.


Contents:

 

Section I: Basics of Neural Networks and Deep Learning
An introduction to neural network and deep learning
An introduction to deep reinforcement learning
Engineering issues and software packages for NN and DL
A literature review of DL for MIA

Section II: Deep Learning Algorithms for Detection
Anatomy detection using marginal space deep learning
Deep neural networks segment neuronal membranes in electron microscopy images
Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection
Body part recognition using multi-stage deep learning
An ensemble of CNNs for polyp detection using spatio-temporal information
Detection of fetal ultrasound standard plane and intervertebral discs

Section III: Deep Learning Algorithms for Image Segmentation and Registration
U-net: Convolutional networks in biomedical image segmentation
Contextual NN for medical image detection and segmentation & Mitosis detection
Deep Organ and 2.5D representation
Deformable MR prostate segmentation using deep learning and sparse patch matching
Scalable high performance image registration framework by unsupervised deep feature representation learning

Section IV: Deep Learning Algorithms for Computer-Aided Diagnosis
Transfer deep learning for chest X-ray
Deep learning for breast CAD
Deep learning for Lung and chest / CT
Breast/ Mammograph/ risk scoring using deep learning
Randomized denoising autoencoders for smaller and efficient imaging based AD clinical trials
Computer-aided classification of lung nodules on computed tomography images via deep learning technique
Using deep learning to detect small intestine disorders

Section V: Others
Image synthesis using deep network
Text/Image mining on a large-scale radiology image database
Reinforcement learning for medical image analysis
Microscopy cell counting with fully convolutional regression networks
Deep voting and beyond classification for microscopy image analysis

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