Deep Learning and Convolutional Neural Networks for Medical Image Computing

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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…mehr

Produktbeschreibung
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.
  • Produktdetails
  • Advances in Computer Vision and Pattern Recognition
  • Verlag: Springer, Berlin; Springer International Publishing
  • Best.Nr. des Verlages: .978-3-319-42998-4, 978-3-319-42998-4
  • 1st ed. 2017
  • 2017
  • Ausstattung/Bilder: 1st ed. 2017. 2017. xiii, 326 S. 17 SW-Abb., 100 Farbabb. 235 mm
  • Englisch
  • Abmessung: 244mm x 159mm x 22mm
  • Gewicht: 714g
  • ISBN-13: 9783319429984
  • ISBN-10: 3319429981
  • Best.Nr.: 45199105
Autorenporträt
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.
Inhaltsangabe
Part I: Review 1. Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective Ronald M. Summers 2. Review of Deep Learning Methods in Mammography
Cardiovascular and Microscopy Image Analysis Gustavo Carneiro
Yefeng Zheng
Fuyong Xing
and Lin Yang · Part II: Detection and Localization 3. Efficient False-Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation Holger R. Roth
Le Lu
Jiamin Liu
Jianhua Yao
Ari Seff
Kevin Cherry
Lauren Kim
and Ronald M. Summers 4. Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning Yefeng Zheng
David Liu
Bogdan Georgescu
Hien Nguyen
and Dorin Comaniciu 5. A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set Fujun Liu and Lin Yang 6. Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers Jun Xu
Chao Zhou
Bing Lang
and Qingshan Liu 7. Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation
Unordered Pooling and Cross-Dataset Learning Mingchen Gao
Ziyue Xu
Le Lu
and Daniel J. Mollura 8. Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging Hoo-Chang Shin
Holger R. Roth
Mingchen Gao
Le Lu
Ziyue Xu
Isabella Nogues
Jianhua Yao
Daniel Mollura
and Ronald M. Summers 9. Cell Detection with Deep Learning Accelerated by Sparse Kernel Junzhou Huang and Zheng Xu 10. Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition Christian Baumgartner
Ozan Oktay
and Daniel Rueckert 11. On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging Nima Tajbakhsh
Jae Y. Shin
Suryakanth R. Gurudu
R. Todd Hurst
Christopher B. Kendall
Michael B. Gotway
and Jianming Liang · Part III: Segmentation 12. Fully Automated Segmentation Using Distance Regularized Level Set and Deep-Structured Learning and Inference Tuan Anh Ngo and Gustavo Carneiro 13. Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms Neeraj Dhungel
Gustavo Carneiro
and Andrew P. Bradley 14. Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context Yefeng Zheng
David Liu
Bogdan Georgescu
Daguang Xu
and Dorin Comaniciu 15. Robust Cell Detection and Segmentation in Histopathological Images using Sparse Reconstruction and Stacked Denoising Autoencoders Hai Su
Fuyong Xing
Xiangfei Kong
Yuanpu Xie
Shaoting Zhang and Lin Yang 16. Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling Amal Farag
Le Lu
Holger R. Roth
Jiamin Liu
Evrim Turkbey
and Ronald M. Summers · Part IV: Big Dataset and Text-Image Deep Mining 17. Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database Hoo-Chang Shin
Le Lu
Lauren Kim
Ari Seff
Jianhua Yao
and Ronald Summers