Fr. 199.00

Deep Learning and Convolutional Neural Networks for Medical Image Computing - Precision Medicine, High Performance and Large-Scale Datasets

Inglese · Copertina rigida

Spedizione di solito entro 6 a 7 settimane

Descrizione

Ulteriori informazioni

This book 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. 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 of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; 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.

Sommario

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 RadiologyImage Database.

Info autore

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.

Riassunto

This book 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. 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 of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; 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.

Testo aggiuntivo

“This book … is very suitable for students, researchers and practitioner. In addition, the book provides an important and useful reference for experienced researchers on particular aspects of deep learning based medical image analysis.” (Guang Yang, IAPR Newsletter, Vol. 41 (2), April, 2019)

Relazione

"This book ... is very suitable for students, researchers and practitioner. In addition, the book provides an important and useful reference for experienced researchers on particular aspects of deep learning based medical image analysis." (Guang Yang, IAPR Newsletter, Vol. 41 (2), April, 2019)

Dettagli sul prodotto

Con la collaborazione di Gustavo Carneiro (Editore), Gustavo Carneiro et al (Editore), Le Lu (Editore), Lin Yang (Editore), Yefen Zheng (Editore), Yefeng Zheng (Editore)
Editore Springer, Berlin
 
Lingue Inglese
Formato Copertina rigida
Pubblicazione 07.08.2017
 
EAN 9783319429984
ISBN 978-3-31-942998-4
Pagine 326
Dimensioni 158 mm x 22 mm x 243 mm
Peso 714 g
Illustrazioni XIII, 326 p. 117 illus., 100 illus. in color.
Serie Advances in Pattern Recognition
Advances in Computer Vision and Pattern Recognition
Advances in Computer Vision and Pattern Recognition
Categorie Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Software applicativo

B, Artificial Intelligence, Radiology, computer science, Computer Vision, Image Processing and Computer Vision, Imaging / Radiology, Optical data processing, Medical imaging, Mathematical modelling, Neural networks (Computer science)

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