Deep Learning for Medical Image AnalysisDeep 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.
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Contents
Part 2 Medical Image Detection and Recognition | 53 |
| 55 | |
4 MultiInstance MultiStage Deep Learning for Medical Image Recognition | 83 |
5 Automatic Interpretation of Carotid IntimaMedia Thickness Videos Using Convolutional Neural Networks | 105 |
6 Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images | 133 |
7 Deep Voting and Structured Regression for Microscopy Image Analysis | 155 |
Part 3 Medical Image Segmentation | 177 |
8 Deep Learning Tissue Segmentation in Cardiac Histopathology Images | 179 |
12 Convolutional Neural Networks for Robust and RealTime 2D3D Registration | 271 |
Part 5 ComputerAided Diagnosis and Disease Quantification | 297 |
13 Chest Radiograph Pathology Categorization via Transfer Learning | 299 |
14 Deep Learning Models for Classifying Mammogram Exams Containing Unregistered MultiView Images and Segmentation Maps of Lesions | 321 |
| 341 | |
Part 6 Others | 379 |
16 Deep Networks and Mutual Information Maximization for CrossModal Medical Image Synthesis | 381 |
| 405 | |
9 Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching | 197 |
| 223 | |
Part 4 Medical Image Registration | 243 |
11 Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning | 245 |
Other editions - View all
Deep Learning for Medical Image Analysis S. Kevin Zhou,Dinggang Shen,Hayit Greenspan No preview available - 2017 |
Common terms and phrases
algorithm annotations applied approach architecture auto-encoder autoencoder automatic backpropagation Bengio brain images carotid CIMT classification clinical Computer Vision Conference on Computer convolutional layers convolutional networks convolutional neural networks corresponding dataset deep learning deep networks deep neural networks deformable denotes discriminative disease dropout error estimation evaluate extracted F1 score feature maps feature representations filters framework function gradient ground truth handcrafted features hidden layer IEEE IEEE Trans image patches image registration input International Conference iteration label learned feature loss function machine learning medical image analysis MICCAI mitosis multi-modal NeuroImage nodes nonlinear optimal output parameters Pattern Recognition PEHL performance pixel pre-trained prediction proposed prostate random random forest rDrm regions registration methods regression robust samples segmentation selection shown in Fig softmax sparse spatial SSAE stochastic gradient descent task tion training data transformation unsupervised vector Vision and Pattern visual voxel X-ray images
Bibliographic information
| Title | Deep Learning for Medical Image Analysis |
| Editors | S. Kevin Zhou, Hayit Greenspan, Dinggang Shen |
| Publisher | Academic Press, 2017 |
| ISBN | 0128104090, 9780128104095 |
| Length | 458 pages |
| Subjects | › › Computers / Bioinformatics Computers / Enterprise Applications / Business Intelligence Tools Computers / Enterprise Applications / General Computers / Image Processing Computers / Intelligence (AI) & Semantics Computers / Optical Data Processing |
| Export Citation | BiBTeX EndNote RefMan |