Advances in Deep Generative Models for Medical Artificial Intelligence (Studies in Computational Intelligence, 1124)

Preface
Acknowledgements
Contents
About the Editors
Deep Learning Techniques for 3D-Volumetric Segmentation of Biomedical Images
	1 Introduction
	2 Deep Learning for 3D-Volumetric Segmentation of Biomedical Images
	3 CNN-Based Algorithms for 3D-Volumetric Segmentation of Biomedical Images
		3.1 Algorithms for 3D-Volumetric Semantic Segmentation of Biomedical Images
		3.2 Algorithms for 3D-Volumetric Instance Segmentation of Biomedical Images
		3.3 Algorithms for 3D-Volumetric Panoptic Segmentation of Biomedical Images
	4 GAN-Based Algorithms for 3D-Volumetric Segmentation of Biomedical Images
		4.1 Algorithms for 3D-Volumetric Semantic Segmentation of Biomedical Images
		4.2 Algorithms for 3D-Volumetric Instance Segmentation of Biomedical Images
		4.3 Algorithms for 3D-Volumetric Panoptic Segmentation of Biomedical Images
	5 Challenges
		5.1 Limited Data Annotation
		5.2 High Computational Complexity
		5.3 Overfitting
		5.4 Training Time
	6 Conclusion
	References
Analysis of GAN-Based Data Augmentation for GI-Tract Disease Classification
	1 Introduction
	2 Related Work
		2.1 Data Augmentation Approaches for Medical Imaging
	3 Data Augmentation
	4 Types of Image Data Augmentation Techniques
		4.1 Geometric Transformations Based Augmentation
		4.2 Data Augmentation with GANs
	5 Methodology
	6 Results and Discussion
	7 Conclusion
	References
Deep Generative Adversarial Network-Based MRI Slices Reconstruction and Enhancement  for Alzheimer's Stages Classification
	1 Introduction
	2 Related Work
	3 Dataset
	4 Methodology
		4.1 Deep Convolutional GAN (DCGAN)
		4.2 Vanilla GAN (VGAN)
	5 Results and Discussion
	6 Conclusion
	References
Evaluating the Quality and Diversity of DCGAN-Based Generatively Synthesized Diabetic Retinopathy Imagery
	1 Introduction
	2 Related Work
		2.1 GAN-Based Approaches to Addressing Data Imbalance for DR
	3 Methodology
		3.1 DCGAN Architecture
		3.2 Retinal Fundus Imagery
		3.3 Evaluation of GAN-Based Synthetic Imagery
		3.4 Normalization of Evaluation Metrics
		3.5 Classification of PDR Images
		3.6 Correlation of Quality, Diversity, and Classification Performances
	4 Results and Discussion
		4.1 Critical Analysis of Quantitative Evaluation Metrics
		4.2 Evaluation of Synthetic PDR Imagery
		4.3 Assessment of Synthetic Imagery Using Classification Scores
	5 Conclusion
	References
Deep Learning Approaches for End-to-End Modeling of Medical Spatiotemporal Data
	1 Introduction
	2 Spatial Temporal Deep Learning Background
		2.1 Convolutional Neural Networks
		2.2 Recurrent Neural Networks
		2.3 Attention
	3 Medical Imaging Applications
		3.1 Biopotential Imaging
		3.2 Cardiac Imaging
		3.3 Angiography and Perfusion Imaging
		3.4 Functional Magnetic Resonance Imaging
	4 Learning from Small Samples
		4.1 Network Pre-training
		4.2 Regularization
	5 Conclusion
	References
Skin Cancer Classification with Convolutional Deep Neural Networks and Vision Transformers Using Transfer Learning
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Dataset
		3.2 Preprocessing
		3.3 Pre-trained Model Architectures
	4 Results and Discussion
	5 Conclusion
	References
A New CNN-Based Deep Learning Model Approach for Skin Cancer Detection and Classification
	1 Introduction
	2 Related Works
	3 Material and Method
		3.1 Segmentation
		3.2 Classification
	4 Experimental Studies
		4.1 Evaluation Metrics
		4.2 Experimental Segmentation
		4.3 Evaluation Results
	5 Conclusion and Discussion
	References
Machine Learning Based Miscellaneous Objects Detection with Application to Cancer Images
	1 Introduction
	2 The Adaptive Boosting Algorithm (ABA)
	3 Experimental Setup and Results
		3.1 Investigations on Melanoma
		3.2 License Plate Detection (LPD)
		3.3 Vehicle Detection
		3.4 Pedestrian Detection
		3.5 Players' Detection
		3.6 Football Detection
		3.7 Computational Complexity
		3.8 Discussion
		3.9 Future Research Direction
		3.10 Final Remarks
	4 Conclusions
	References
Advanced Deep Learning for Heart Sounds Classification
	1 Introduction
		1.1 Heart Sounds and Auscultation
	2 Datasets
		2.1 PhysioNet 2016
		2.2 PASCAL 2011
	3 Pre-processing of Heart Sounds
	4 Features Extraction
		4.1 Heart Sounds Spectrograms
	5 Classification
		5.1 Convolutional Neural Network
		5.2 Auto-encoders
		5.3 Vision Transformers
		5.4 Transfer-Learning Using Pre-trained Models
	6 Performance Metrics
	7 Results
	8 Conclusions
	References