Deep Learning and Convolutional Neural Networks for Medical Image Computing : Precision Medicine, High Performance and Large-Scale Datasets / edited by Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang.

Format:

Book

Contributor:

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

Zheng, Yefeng, 1975- 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.

Artificial intelligence.

Neural networks (Computer science).

Radiology.

Image Processing and Computer Vision.

Artificial Intelligence.

Mathematical Models of Cognitive Processes and Neural Networks.

Imaging / Radiology.

Local Subjects:

Image Processing and Computer Vision.

Artificial Intelligence.

Mathematical Models of Cognitive Processes and Neural Networks.

Imaging / Radiology.

Physical Description:

1 online resource (XIII, 326 pages) : 117 illustrations, 100 illustrations in color.

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

This timely text/reference presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Topics and features: Highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing Discusses the insightful research experience and views of Dr. Ronald M. Summers in medical imaging-based computer-aided diagnosis and its interaction with deep learning Presents a comprehensive review of the latest research and literature on deep learning for medical image analysis Describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging Examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging Introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database for automated image interpretation This pioneering volume will prove invaluable to researchers and graduate students wishing to employ deep neural network models and representations for medical image analysis and medical imaging applications. Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA. Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA. Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia. Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

Contents:

Part I: Review

Chapter 1. Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective

Chapter 2. Review of Deep Learning Methods in Mammography, Cardiovascular and Microscopy Image Analysis

Part II: Detection and Localization

Chapter 3. Efficient False-Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

Chapter 4. Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning

Chapter 5. A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set

Chapter 6. Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers

Chapter 7. Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning

Chapter 8. Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging

Chapter 9. Cell Detection with Deep Learning Accelerated by Sparse Kernel

Chapter 10. Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition

Chapter 11. On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging

Part III: Segmentation

Chapter 12. Fully Automated Segmentation Using Distance Regularized Level Set and Deep-Structured Learning and Inference

Chapter 13. Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms

Chapter 14. Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context

Chapter 15. Robust Cell Detection and Segmentation in Histopathological Images using Sparse Reconstruction and Stacked Denoising Autoencoders

Chapter 16. Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling

Part IV: Big Dataset and Text-Image Deep Mining

Chapter 17. Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database.

Other Format:

Printed edition:

ISBN:

978-3-319-42999-1

9783319429991

Access Restriction:

Restricted for use by site license.