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Part I. Segmentation
1. Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning
2. Deep Learning for Muscle Pathology Image Analysis
3. 2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scans
4. Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples
5. Unsupervised Domain Adaptation of ConvNets for Medical Image Segmentation via Adversarial Learning
Part II. Detection and Localization
6. Glaucoma Detection Based on Deep Learning Network in Fundus Image
7. Thoracic Disease Identification and Localization with Limited Supervision
8. Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI
9. Automatic Vertebra Labeling in Large-Scale Medical Images Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
10. Anisotropic Hybrid Network for Cross-Dimension Transferable Feature Learning in 3D Medical Images
Part III. Various Applications
11. Deep Hashing and Its Application for Histopathology Image Analysis
12. Tumor Growth Prediction Using Convolutional Networks
13. Deep Spatial-Temporal Convolutional Neural Networks for Medical Image Restoration
14. Generative Low-Dose CT Image Denoising
15. Image Quality Assessment for Population Cardiac Magnetic Resonance Imaging
16. Agent-Based Methods for Medical Image Registration
17. Deep Learning for Functional Brain Connectivity: Are We There Yet?
Part IV. Large-Scale Data Mining and Data Synthesis
18. ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases
19. Automatic Classification and Reporting of Multiple Common Thorax Diseases Using Chest Radiographs
20. Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database
21. Simultaneous Super-Resolution and Cross-Modality Synthesis in Magnetic Resonance Imaging
Index

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Deep learning and convolutional neural networks for medical imaging and clinical informatics 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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0002613336 006.32 -A20-1 서울관 서고(열람신청 후 1층 대출대) 이용가능

출판사 책소개

알라딘제공
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.

 




New feature

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.



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