State of the Art in Neural Networks and Their Applications: Volume 1

Title-page_2021_State-of-the-Art-in-Neural-Networks-and-their-Applications
	State of the Art in Neural Networks and Their Applications
Copyright_2021_State-of-the-Art-in-Neural-Networks-and-their-Applications
	Copyright
Dedication_2021_State-of-the-Art-in-Neural-Networks-and-their-Applications
	Dedication
Contents_2021_State-of-the-Art-in-Neural-Networks-and-their-Applications
	Contents
List-of-Contributo_2021_State-of-the-Art-in-Neural-Networks-and-their-Applic
	List of Contributors
Biographies_2021_State-of-the-Art-in-Neural-Networks-and-their-Applications
	Biographies
Acknowledgments_2021_State-of-the-Art-in-Neural-Networks-and-their-Applicati
	Acknowledgments
Chapter-1---Computer-aided-detection-of-abno_2021_State-of-the-Art-in-Neural
	1 Computer-aided detection of abnormality in mammography using deep object detectors
		1.1 Introduction
		1.2 Literature review
		1.3 Methodology
			1.3.1 Architectures of deep convolutional neural networks and deep object detectors
			1.3.2 Abnormality detection with faster R-convolutional neural networks
			1.3.3 Abnormality detection with YOLO
		1.4 Experimental results
			1.4.1 Data preparation
			1.4.2 Abnormality detection with faster R-convolutional neural networks
			1.4.3 Abnormality detection with YOLO
			1.4.4 Results comparison
		1.5 Discussions
		1.6 Conclusion
		References
Chapter-2---Detection-of-retinal-abnormaliti_2021_State-of-the-Art-in-Neural
	2 Detection of retinal abnormalities in fundus image using CNN deep learning networks
		2.1 Introduction
		2.2 Earlier screening and diagnosis of ocular diseases with CNN deep learning networks
			2.2.1 Glaucoma
				2.2.1.1 Methods and materials
				2.2.1.2 Deep learning neural-network architectures for glaucoma screening and diagnosis
				2.2.1.3 Application and evaluation on earlier glaucoma screening and diagnosis—classification
					2.2.1.3.1 Fundus image glaucoma classification
					2.2.1.3.2 Optical coherence tomography image glaucoma classification
				2.2.1.4 Datasets used in glaucoma diagnosis
			2.2.2 Age-related macular degeneration
				2.2.2.1 Methods and materials
				2.2.2.2 Deep learning–based methods for age-related macular degeneration detection and grading
			2.2.3 Diabetic retinopathy
				2.2.3.1 Methods and materials
				2.2.3.2 Deep learning–based methods for diabetic retinopathy detection and grading
				2.2.3.3 Dataset used diabetic retinopathy diagnosis
			2.2.4 Cataract
				2.2.4.1 Methods and materials
				2.2.4.2 Deep learning–based methods for cataract detection and grading
		2.3 Deep learning–based smartphone for detection of retinal abnormalities
			2.3.1 Smartphone-captured fundus image evaluation
			2.3.2 Deep learning–based method of ocular pathology detection from smartphone-captured fundus image
		2.4 Discussion
		2.5 Conclusion
		References
Chapter-3---A-survey-of-deep-learning-based-_2021_State-of-the-Art-in-Neural
	3 A survey of deep learning-based methods for cryo-electron tomography data analysis
		3.1 Introduction
		3.2 Deep learning-based methods
			3.2.1 Detection and segmentation
			3.2.2 Classification
			3.2.3 Others
		3.3 Conclusion
		References
Chapter-4---Detection--segmentation--and-numbering-_2021_State-of-the-Art-in
	4 Detection, segmentation, and numbering of teeth in dental panoramic images with mask regions with convolutional neural ne...
		4.1 Introduction
		4.2 Related work
		4.3 Fédération Dentaire Internationale tooth numbering system
		4.4 The method
			4.4.1 Implementation details
				4.4.1.1 Tooth numbering
		4.5 Experimental analysis
			4.5.1 Dataset
			4.5.2 Evaluation
			4.5.3 Results
		4.6 Discussion and conclusions
		References
Chapter-5---Accurate-identification-of-renal-tr_2021_State-of-the-Art-in-Neu
	5 Accurate identification of renal transplant rejection: convolutional neural networks and diffusion MRI
		5.1 Introduction
		5.2 Methods
			5.2.1 Kidney segmentation
			5.2.2 Feature extraction
			5.2.3 Renal transplant classification using deep convolutional neural network
		5.3 Experimental results
		5.4 Conclusion
		Acknowledgments
		References
Chapter-6---Applications-of-the-ESPNet-_2021_State-of-the-Art-in-Neural-Netw
	6 Applications of the ESPNet architecture in medical imaging
		6.1 Introduction
		6.2 Background
			6.2.1 Standard convolution
			6.2.2 Dilated convolution
		6.3 The ESPNet architecture
			6.3.1 Efficient spatial pyramid unit
				6.3.1.1 Hierarchical feature fusion for degridding in the efficient spatial pyramid unit
			6.3.2 Segmentation architecture
		6.4 Experimental results
			6.4.1 Breast biopsy whole slide image dataset
				6.4.1.1 Dataset
				6.4.1.2 Training
				6.4.1.3 Segmentation results
				6.4.1.4 Skip connections
				6.4.1.5 Pyramidal spatial pooling as a decoding unit
				6.4.1.6 Comparison with state-of-the-art methods
				6.4.1.7 Tissue-level segmentation masks for computer-aided diagnosis
			6.4.2 Brain tumor segmentation
				6.4.2.1 Dataset
				6.4.2.2 Training
				6.4.2.3 Results
			6.4.3 Other applications
		6.5 Conclusion
		Acknowledgment
		References
Chapter-7---Achievements-of-neural-netwo_2021_State-of-the-Art-in-Neural-Net
	7 Achievements of neural network in skin lesions classification
		7.1 Introduction
		7.2 Literature review
		7.3 Background
		7.4 Dataset
		7.5 Methodology
		7.6 Results and discussion
		7.7 Conclusion
		References
Chapter-8---A-computer-aided-diagnosis-system-f_2021_State-of-the-Art-in-Neu
	8 A computer-aided diagnosis system for breast cancer molecular subtype prediction in mammographic images
		8.1 Introduction
		8.2 Background
			8.2.1 Breast cancer detection
			8.2.2 Breast tumor segmentation
			8.2.3 Shape classification
		8.3 Datasets
		8.4 Methodology
			8.4.1 Modified Faster R-CNN for breast tumor detection
			8.4.2 Breast tumor segmentation using conditional generative adversarial network
			8.4.3 Shape descriptor using convolutional neural network
			8.4.4 Breast cancer molecular subtypes correlation to the tumor shape
		8.5 Conclusion
		References
Chapter-9---Computer-aided-diagnos_2021_State-of-the-Art-in-Neural-Networks-
	9 Computer-aided diagnosis of renal masses
		9.1 Introduction
		9.2 Segmentation of kidneys
			9.2.1 Convolutional neural network
				9.2.1.1 Convolutional layer
				9.2.1.2 Detection layer
				9.2.1.3 Pooling layer
			9.2.2 U-Net
			9.2.3 Performance evaluation of the algorithm
			9.2.4 Deep learning–based methods for automated segmentation of kidney
		9.3 Kidney tumor localization
		9.4 Differentiation of malignant versus benign renal masses
		9.5 Future perspectives
		References
Chapter-10---Early-identification-of-acute-re_2021_State-of-the-Art-in-Neura
	10 Early identification of acute rejection for renal allografts: a machine learning approach
		Acknowledgment
		10.1 Introduction
		10.2 Methods
			10.2.1 Diffusion-weighted image markers
			10.2.2 Clinical biomarkers
			10.2.3 Integration process of clinical with imaging biomarkers
		10.3 Experimental results
		10.4 Conclusion
		References
Chapter-11---Deep-learning-for-computer-ai_2021_State-of-the-Art-in-Neural-N
	11 Deep learning for computer-aided diagnosis in ophthalmology: a review
		11.1 Introduction
			11.1.1 The burden of eye disease
			11.1.2 Imaging and image analysis
			11.1.3 Deep learning: an emerging state-of-the-art
		11.2 Deep learning: the methods
			11.2.1 Reference standards
			11.2.2 Preprocessing and augmentation
			11.2.3 Architectures, transfer learning, and ensembling
			11.2.4 Loss functions and performance metrics
		11.3 Limitations of the state-of-the-art
			11.3.1 Trustworthiness and transparency
			11.3.2 Uncertainty estimation
			11.3.3 Explainability and interpretability
		11.4 Beyond convolutional neural networks
			11.4.1 Generative adversarial networks
			11.4.2 Capsule networks
		11.5 Conclusion
		References
Chapter-12---Deep-learning-for-ophthalmol_2021_State-of-the-Art-in-Neural-Ne
	12 Deep learning for ophthalmology using optical coherence tomography
		12.1 Introduction
		12.2 Optical coherence tomography
			12.2.1 Variations of optical coherence tomography systems
				12.2.1.1 Time-domain optical coherence tomography
				12.2.1.2 Spectral domain optical coherence tomography
				12.2.1.3 Polarization-sensitive optical coherence tomography
				12.2.1.4 Swept-source optical coherence tomography
			12.2.2 Optical coherence tomography datasets
			12.2.3 Advantages and disadvantages of optical coherence tomography imaging
		12.3 Retinal biomarkers and diseases
			12.3.1 Important retinal diseases
				12.3.1.1 Diabetic retinopathy and diabetic macular edema
				12.3.1.2 Glaucoma
				12.3.1.3 Age-related macular degeneration
			12.3.2 Biomarker use in disease analysis
		12.4 Traditional approaches for ophthalmic diagnosis
			12.4.1 Image-processing fundamentals
			12.4.2 Feature extraction fundamentals
			12.4.3 Traditional classifiers
			12.4.4 Applications
				12.4.4.1 Denoising
				12.4.4.2 Segmentation
				12.4.4.3 Classification
		12.5 Deep learning approaches to optical coherence tomography analysis
			12.5.1 Convolutional neural network applications
			12.5.2 Autoencoder applications
			12.5.3 Generative adversarial network applications
		12.6 Final thoughts
		Acknowledgment
		Conflict of interest
		References
		Further reading
Chapter-13---Generative-adversarial-n_2021_State-of-the-Art-in-Neural-Networ
	13 Generative adversarial networks in medical imaging
		13.1 Introduction
		13.2 Applications in medical imaging
			13.2.1 Localization and classification
			13.2.2 CS-MRI reconstruction
			13.2.3 Customized medical products
		13.3 Conclusions
		References
Chapter-14---Deep-learning-from-small-label_2021_State-of-the-Art-in-Neural-
	14 Deep learning from small labeled datasets applied to medical image analysis
		14.1 Introduction
		14.2 Cross-modality deep learning
			14.2.1 Feature adaptation-based cross-modality learning
			14.2.2 Data augmentation-based cross-modality learning
		14.3 Example of cross-domain adaptation-based segmentation: lung tumor segmentation from MRI
			14.3.1 Study population and datasets
			14.3.2 Method
		14.4 Results
		14.5 Future outlook and discussion
		14.6 Conclusion
		References
Index_2021_State-of-the-Art-in-Neural-Networks-and-their-Applications
	Index