Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics / edited by Le Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang.

Format:

Book

Contributor:

Lü, Le (Of Johns Hopkins University), editor.

Wang, Xiaosong (Of NVIDIA Corporation), editor.

Carneiro, Gustavo, editor.

Yang, Lin (Editor), editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Advances in computer vision and pattern recognition 2191-6586

Advances in Computer Vision and Pattern Recognition, 2191-6586

Language:

English

Subjects (All):

Optical data processing.

Radiology.

Artificial intelligence.

Neural networks (Computer science).

Image Processing and Computer Vision.

Imaging / Radiology.

Artificial Intelligence.

Mathematical Models of Cognitive Processes and Neural Networks.

Local Subjects:

Image Processing and Computer Vision.

Imaging / Radiology.

Artificial Intelligence.

Mathematical Models of Cognitive Processes and Neural Networks.

Physical Description:

1 online resource (XI, 461 pages) : 177 illustrations, 156 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.

Contents:

Chapter 1. Clinical Report Guided Multi-Sieving Deep Learning for Retinal Microaneurysm Detection

Chapter 2. Optic Disc and Cup Segmentation Based on Multi-label Deep Network for Fundus Glaucoma Screening

Chapter 3. Thoracic Disease Identification and Localization with Limited Supervision

Chapter 4. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Chapter 5. TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays

Chapter 6. Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Chapter 7. Deep Reinforcement Learning based Attention to Detect Breast Lesions from DCE-MRI

Chapter 8. Deep Convolutional Hashing for Low Dimensional Binary Embedding of Histopathological Images

Chapter 9. Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning

Chapter 10. Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation

Chapter 11. Pancreas

Chapter 12. Multi-Organ

Chapter 13. Convolutional Invasion and Expansion Networks for Tumor Growth Prediction

Chapter 14. Cross-Modality Synthesis in Magnetic Resonance Imaging

Chapter 15. Image Quality Assessment for Population Cardiac MRI

Chapter 16. Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

Chapter 17. Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

Chapter 18. Automatic Vertebra Labeling in Large-Scale Medical Images using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

Chapter 19. 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

Chapter 20. Multi-Agent Learning for Robust Image Registration

Chapter 21. Deep Learning in Magnetic Resonance Imaging of Cardiac Function

Chapter 22. Automatic Vertebra Labeling in Large-Scale Medical Images using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

Chapter 23. Deep Learning on Functional Connectivity of Brain: Are We There Yet?.

Other Format:

Printed edition:

ISBN:

978-3-030-13969-8

9783030139698

9783030139681

9783030139704

9783030139711

Access Restriction:

Restricted for use by site license.