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

:	Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang
:	Deep Learning and Convolutional Neural Networks for Medical Image Computing Precision Medicine, High Performance and Large-Scale Datasets
:	Springer-Verlag
:	9783319429991
:	Advances in Computer Vision and Pattern Recognition
:	1
:	CHF 144.90
:

:	Anwendungs-Software
:	English

:	327
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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.

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.

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Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

	Preface	6
	Overview and Goals	7
	Organization and Features	8
	Target Audience	9
	Acknowledgements	10
	Contents	11
	Part I Review	14
	1 Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective	15
	References	20
	2 Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis	23
	2.1 Introduction on Deep Learning Methods in Mammography	23
	2.2 Deep Learning Methods in Mammography	24
	2.3 Summary on Deep Learning Methods in Mammography	26
	2.4 Introduction on Deep Learning for Cardiological Image Analysis	26
	2.5 Deep Learning-Based Methods for Heart Segmentation	28
	2.6 Deep Learning-Based Methods for Vessel Segmentation	29
	2.7 Introduction to Microscopy Image Analysis	31
	2.8 Deep Learning Methods	33
	2.9 Microscopy Image Analysis Applications	34
	2.10 Discussions and Conclusion on Deep Learning for Microscopy Image Analysis	34
	References	38
	Part II Detection and Localization	45
	3 Efficient False Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation	46
	3.1 Introduction	47
	3.2 Related Work	47
	3.2.1 Cascaded Classifiers in CADe	48
	3.3 Methods	48
	3.3.1 Convolutional Neural Networks	48
	3.3.2 A 2D or 2.5D Approach for Applying ConvNets to CADe	50
	3.3.3 Random View Aggregation	52
	3.3.4 Candidate Generation	52
	3.4 Results	53
	3.4.1 Computer-Aided Detection Data Sets	53
	3.5 Discussion and Conclusions	54
	References	56
	4 Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning	60
	4.1 Introduction	60
	4.2 Training Shallow Network with Separable Filters	63
	4.3 Training Sparse Deep Network	66
	4.4 Robust Detection by Combining Multiple Features	67
	4.5 Experiments	68
	4.6 Conclusions	70
	References	71
	5 A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set	73
	5.1 Introduction	73
	5.2 Methodology	75
	5.2.1 Cell Detection Using MWIS	75
	5.2.2 Deep Convolutional Neural Network	76
	5.3 Experiments	78
	5.4 Conclusion	81
	References	81
	6 Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers	83
	6.1 Introduction	84
	6.2 Previous Works	85
	6.2.1 Previous Works on Deep Learning for Histological Image Analysis	86
	6.2.2 Previous Works on Nuclear Atypia Scoring	87
	6.2.3 Previous Works on Epithelial and Stromal Segmentation	88
	6.3 Deep Learning for Nuclear Atypia Scoring	88
	6.3.1 CN Model for Nuclear Atypia Scoring	90
	6.3.2 Integration MR-CN with Combination Voting Strategies for NAS	91
	6.4 Deep Learning for Epithelial and Stromal Tissues Segmentation	94
	6.4.1 The Deep Convolutional Neural Networks	94
	6.4.2 Generating Training and Testing Samples	94
	6.4.3 The Trained CN for the Discrimination of EP and ST Regions	95
	6.5 Experimental Setup	96
	6.5.1 Data Set	97
	6.5.2 Comparison Strategies	98
	6.5.3 Computational and Implemental Consideration	99
	6.6 Results and Discussion	99
	6.6.1 Qualitative Results	99
	6.6.2 Quantitative Results	100
	6.7 Concluding Remarks	102
	References	102
	7 Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning	106
	7.1 Introduction	106
	7.2 Methods	109
	7.2.1 Segmentation Label Propagation	110
	7.2.2 Multi-label ILD Regression	112
	7.3 Experiments and Discussion	114
	7.3.1 Segmentation Label Propagation	114
	7.3.2 Multi-label ILD Regression	116
	7.4 Conclusion	118
	References	119
	8 Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging	121
	8.1 Introduction	122
	8.2 Datasets and Related Work	124
	8.3 Methods	127
	8.3.1 Co