Deep learning and convolutional neural networks for medical imaging and clinical informatics / Le Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang, editors.
Lu, Le, editor.; Wang, Xiaosong, editor.; Carneiro, Gustavo, editor.; Yang, Lin, 1992- editor.
2019
Q325.5
Available Online

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Title Deep learning and convolutional neural networks for medical imaging and clinical informatics / Le Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang, editors.
ISBN 9783030139698 (electronic book)
3030139697 (electronic book)
9783030139681
DOI https://doi.org/10.1007/978-3-030-13969-8
https://doi.org/10.1007/978-3-030-13
Published Cham, Switzerland : Springer, 2019.
Language English
Description 1 online resource (xi, 461 pages) : illustrations.
Item Number 10.1007/978-3-030-13969-8 doi
10.1007/978-3-030-13
Call Number Q325.5
Dewey Decimal Classification 006.3/2
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 books 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.
Note Includes index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed September 27, 2019).
Added Author Lu, Le, editor.
Wang, Xiaosong, editor.
Carneiro, Gustavo, editor.
Yang, Lin, 1992- editor.
Series Advances in computer vision and pattern recognition.
Available in Other Form Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
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Record Appears in Online Resources > Ebooks
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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?.

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