Preface	6
	Contents	8
	Part I Overview and Issues	10
	Deep Learning in Medical Image Analysis	11
	Introduction	11
	Deep Learning for Medical Image Analysis and CAD	12
	Challenges in Deep-Learning-Based CAD	15
	Data Collection	17
	Transfer Learning	19
	Data Augmentation	23
	Training, Validation, and Independent Testing	24
	Acceptance Testing, Preclinical Testing, and User Training	24
	Quality Assurance and Performance Monitoring	25
	Interpretability of CAD/AI Recommendations	26
	Summary	26
	Medical Image Synthesis via Deep Learning	30
	Introduction	30
	Deep Learning Models for Medical Image Synthesis	33
	Convolutional Neural Networks	33
	Generative Adversarial Networks	34
	Within-Modality Synthesis	35
	3D cGAN	36
	Framework	36
	Experimental Results	36
	Locality Adaptive Multi-Modality GANs	38
	Framework	39
	Experimental Results	40
	Cross-Modality Synthesis	41
	3D cGAN with Subject-Specific Local Adaptive Fusion	41
	Framework	42
	Experimental Results	43
	Edge-Aware GANs	43
	Framework	44
	Experimental Results	46
	Conclusion	47
	Part II Applications: Screening and Diagnosis	52
	Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation	53
	Background of Lung Diseases	53
	Introduction	54
	Methods	54
	Classification of Lung Abnormalities	54
	Detection of Lung Abnormalities	58
	Segmentation of Lung Abnormalities	59
	Conclusion	62
	Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram	65
	Introduction	66
	Related Work	67
	Materials and Methods	68
	Dataset	68
	Datasets Preparation: Training, Validation, and Testing	69
	Preprocessing	69
	Data Balancing and Augmentation	69
	Initialization of Trainable Parameters for Deep Learning Models	70
	Breast Lesion Detection via YOLO	70
	Breast Lesion Segmentation via FrCN	70
	Breast Lesion Classification via Three Convolutional Neural Networks	71
	Experimental Settings	72
	Detection Experimental Settings	72
	Segmentation Experimental Settings	72
	Classification Experimental Settings	72
	Implementation Environment	73
	Experimental Results and Discussion	73
	Evaluation Metrics	73
	Breast Lesion Detection Results	73
	Breast Lesion Segmentation Results	73
	Breast Lesion Classification Results	75
	Conclusion	76
	Decision Support System for Lung Cancer Using PET/CT and Microscopic Images	79
	Introduction	79
	Outline of Decision Support System	80
	Automated Detection of Lung Nodules in PET/CT Images Using Convolutional Neural Network and Radiomic Features	81
	Background	81
	Method Overview	81
	Initial Nodule Detection	82
	False Positive Reduction	82
	Classification Using a Convolutional Neural Network	83
	Handcrafted Radiomic Features	83
	Classification	83
	Results	84
	Image Datasets	84
	Evaluation Metrics	84
	Detection Results	84
	Discussion	85
	Automated Malignancy Analysis of Lung Nodules in PET/CT Images Using Radiomic Features	86
	Introduction	86
	Materials and Methods	86
	Image Dataset	86
	Methods Overview	87
	Volume of Interest (VOI) Extraction	87
	Extraction of Characteristic Features	87
	Classification	90
	Results	90
	Discussion	90
	Automated Malignancy Analysis Using Lung Cytological Images	92
	Introduction	92
	Materials and Methods	93
	Image Dataset	93
	Network Architecture	93
	Results and Discussion	94
	Automated Classification of Lung Cancer Types from Cytological Images	94
	Introduction	94
	Materials and Methods	95
	Image Dataset	95
	Network Architecture	96
	Results and Discussion	96
	Conclusion	98
	Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection	101
	Introduction	101
	Proposed Method	103
	Dataset	103
	Lesion Image Generation	103
	Method 1: Synthesis Using Poisson Blending	103
	Method 2: Generation Based on a CT Value Distribution	104
	Method 3: Generation Using DCGANs	105
	Selection of the Region of Interest for Lesion Synthesis	105
	Detection Method	107
	Experiments	108
	Results	109
	Discussion	109
	Conclusion	110
	Retinopathy Analysis Based on Deep ConvolutionNeural Network	113
	Introduction	113
	General Arteriolar Narrowing Detection	114
	Blood Vessel Extraction	114
	Related Works	114
	Database	115
	Preprocessing	115
	Blood Vessel Extraction Using DCNN	116
	Detection of Arteriolar Narrowing Using AVR	117
	Related Works	117
	Database	118
	Classification of Arteries and Veins	118
	AVR Measurement	119
	Microaneurysm Detection	120
	Related Work	120
	Database	121
	Methods	121
	Preprocessing	121
	Microaneurysm Detection Based on DCNN	122
	Reducing the Number of False Positives	123
	Examination	124
	Conclusion	124
	Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis	127
	Introduction	127
	Related Works	129
	NFLD Detection	129
	Background	129
	Proposed Method	130
	Segmentation Network	130
	Detection Network	131
	Combined Method	131
	Dataset	131
	Preprocessing	133
	Evaluation	133
	Results	133
	Discussion	133
	Optic Disc Analysis	135
	Background	135
	Methods	135
	Dataset	136
	Results	136
	Discussion	136
	Summary	137
	Part III Applications: Emerging Opportunities	139
	Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches	140
	Introduction	141
	Issue of Deep Learning for CT Image Segmentation	141
	Two Approaches for Multiple Organ Segmentations Using 2D and 3D Deep CNNs on CT Images	142
	Overview	142
	Deep Learning Anatomical Structures on 2D Sectional Images	142
	Deep Learning Local Appearances of Multiple Organs on 3D CT Images	143
	Conventional Image Segmentation Approach	145
	Results	145
	Discussions	147
	Segmentation Performances	147
	Training Protocol and Transfer Learning	148
	Comparison to Conventional Methods	149
	Computational Efficiency	150
	Conclusion	151
	Techniques and Applications in Skin OCT Analysis	153
	Introduction	154
	Skin Layer Segmentation in OCT	154
	Applications: Roughness, ET	160
	Deep Convolutional Networks in Skin Imaging	161
	Deep Learning for Classification of Dermoscopy Images	162
	Deep Learning for Classification of Full Field OCT Images	162
	Classification of Cross-Sectional OCT 2D Scans	162
	Semantic Segmentation in Cross-Sectional OCT Images	164
	Challenges	164
	Conclusions	165
	Deep Learning Technique for Musculoskeletal Analysis	168
	Importance of Musculoskeletal Analysis and Skeletal Muscle Analysis	168
	Musculoskeletal Recognition by Handcrafted Features and Its Limitations	169
	Skeletal Muscle Segmentation Using Deep Learning	170
	Whole-Body Muscle Analysis Using Deep Learning	174
	Fusion of Deep Learning and Handcrafted Features in Skeletal Muscle Modeling	176
	Conclusion	177
	Index	180