Machine Learning for Medical Applications

Name: Machine Learning for Medical Applications | Computer Vision, Image Processing, Disease Detection
Brand: De Gruyter
Price: 164.95 EUR
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Computer Vision, Image Processing, Disease Detection

Ranjith Rajamanickam Amit Sharma Dhivya Ranjith J. Paulo Davim(Editor)

De Gruyter (Publisher)

1st Edition

Published on 1. September 2025

X, 532 pages

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978-3-11-220519-8 (ISBN)

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Contents
List of contributors
Deep learning in computer vision
1 Introduction
2 Overview of deep learning
2.1 Definition and scope of deep learning
2.2 Historical development and milestones
2.3 Key concepts and terminology
2.4 Differences between deep learning and traditional machine learning
2.5 Advantages and limitations of deep learning
3 Evolution of deep learning in computer vision
3.1 Early neural networks and their impact
3.2 The breakthrough of CNNs
3.3 Key innovations in deep learning architectures (e.g., AlexNet and VGGNet)
3.4 Major competitions and benchmarks (e.g., ImageNet and COCO)
3.5 The role of hardware advancements in deep learning
4 Core components of deep learning models
4.1 Neural network layers: input, hidden, and output layers
4.2 Activation functions: ReLU, sigmoid, and tanh
4.3 Convolutional layers and operations
4.4 Pooling layers and their functions
4.5 Fully connected layers and their role
5 Training deep learning models for computer vision
5.1 Data preparation and augmentation techniques
5.1.1 Data preparation
5.1.2 Data augmentation
5.2 Loss functions and optimization algorithms
5.2.1 Optimization algorithms
5.3 Gradient descent and backpropagation
5.3.1 Gradient descent
5.3.2 Backpropagation
5.4 Regularization techniques to prevent overfitting
5.5 Hyperparameter tuning and model evaluation
5.5.1 Model evaluation
6 Impact of deep learning on computer vision applications
6.1 Improvements in image classification accuracy
6.2 Advances in object detection and localization
6.3 Enhanced performance in image segmentation tasks
6.4 Innovations in generative models for image synthesis
6.5 Applications in real-time computer vision systems
7 Conclusion
References
Deep learning for medical image segmentation
1 Introduction
2 Definition and scope of medical image segmentation
2.1 What is medical image segmentation?
2.2 Scope and applications
2.2.1 Clinical applications
2.2.2 Research applications
2.3 Importance in healthcare
2.4 Segmentation tasks and challenges
2.4.1 Challenges in segmentation
2.5 Current trends and innovations
3 Traditional segmentation techniques
3.1 Thresholding methods
3.1.1 Global thresholding
3.1.2 Adaptive thresholding
3.1.3 Multilevel thresholding
3.1.4 Challenges and limitations
3.2 Region-based methods
3.2.1 Area development
3.2.2 Region merging
3.2.3 Watershed segmentation
3.2.4 Region splitting and merging
3.2.5 Active contours (snakes)
3.3 Edge detection techniques
3.4 Clustering-based approaches
3.4.1 k-Means clustering
3.4.2 Gaussian mixture models (GMMs)
3.4.3 Clustering fuzzy c-means (FCM)
3.4.4 Mean shift clustering
3.4.5 Watershed algorithm
3.5 Model-based methods
3.5.1 Active contours and snakes
3.5.2 Level set methods
3.5.3 Deformable models
3.5.4 Statistical shape models
3.5.5 Template matching
4 Evolution toward deep learning-based methods
4.1 Limitations of traditional methods
4.1.1 Sensitivity to noise and artifacts
4.1.2 Reduced flexibility to deal with complex hierarchy
4.1.3 Time analysis and dimension of growth
4.2 Introduction to machine learning in segmentation
4.2.1 Historical background and original machine learning techniques
4.2.2 Segmentations by supervised learning and segmentations by unsupervised learning
4.2.3 Limitations of early machine learning models
4.2.4 Migration of problems to deep learning paradigms
4.2.5 Impact and advancements
4.3 Transition to deep learning
4.3.1 Details of neural networks
4.3.2 Introduction of U-Net and its variants
4.3.3 Impact of GANs
4.3.4 Transfer learning and pretrained model integration
4.3.5 Challenges and opportunities in deep learning
4.4 Deep learning architectures for segmentation
4.4.1 Convolutional neural networks
4.4.2 U-Net architecture
4.4.3 Attention mechanisms
4.4.4 DeepLab series
4.4.5 Transformers for segmentation
4.5 Adoption in clinical practice
4.5.1 Education into clinical practice
4.5.2 Effects on correctness of diagnosis
4.5.3 Challenges in implementation
4.5.4 Regulatory and ethical of healthcare
4.5.5 Examples and testaments
4.5.6 Future prospects
5 Overview of imaging modalities and their characteristics
5.1 Magnetic resonance imaging (MRI)
5.1.1 Principles of MRI technology
5.1.2 Applications in soft tissue imaging
5.1.3 Imaging sequences and contrast mechanisms
5.1.4 Issues in MRI segmentation
5.1.5 Recent improvements and change
5.2 Computed tomography
5.2.1 Principles of CT imaging
5.2.2 Advantages of CT imaging
5.2.3 Segmentation in CT imaging
5.3 Ultrasound imaging
5.3.1 Principles of ultrasound imaging
5.3.2 Tables in clinical application
5.3.3 Advantages of ultrasound imaging
5.3.4 Challenges and limitations
5.3.5 Advances and innovations
5.4 X-ray and other modalities
5.4.1 Characteristics and applications
5.4.2 Computed tomography
5.4.2.1 Characteristics and applications
5.4.3 Ultrasound imaging
5.4.3.1 Characteristics and applications
5.4.4 Magnetic resonance imaging
5.4.4.1 Characteristics and applications
5.4.5 Positron emission tomography
5.4.5.1 Characteristics and applications
5.5 Multimodal imaging approaches
5.5.1 Synergy of complementary information
5.5.2 Methods of using multiple modalities
5.5.3 Challenges and considerations
6 Importance of accurate segmentation in clinical practice
6.1 Improving diagnostic accuracy
6.1.1 Enhanced detection of pathological features
6.1.2 Refinement of surgical planning
6.1.3 Radiotherapy treatment assistance
6.1.4 Supporting intensive long-term observations
6.2 Facilitating treatment planning
6.2.1 Optimization of radiotherapy
6.2.2 Personalized treatment approaches
6.2.3 Interoperability with complex overview techniques
6.2.4 Supervision and modifying management
6.3 Monitoring disease progression
6.3.1 Monitoring changes in and trends of the disease
6.3.2 Evaluating treatment efficacy
6.3.3 Detecting disease recurrence
6.3.4 Enhancing personalized medicine
6.4 Reducing manual effort and subjectivity
6.4.1 Organizing work to minimize repetitive activities
6.4.2 Minimizing human error
6.4.3 Enhancing reproducibility and consistency
6.4.4 Supporting the quantitative computation
6.5 Potential for personalized medicine
6.5.1 Adapting interventions for the person
6.5.2 Role in precision oncology
6.5.3 Enhancing surgical planning
6.5.4 Observing disease development
6.5.5 Advancements in multiomics integration
6.5.6 Opportunities and threats
7 Conclusion
Reference
Deep learning for image segmentation
1 Introduction
2 Early developments in CNNs for image segmentation
2.1 Introduction to CNNs in computer vision
2.2 The emergence of FCNs
2.3 SegNet
2.4 Advancements with conditional random fields (CRFs)
2.5 Segmentation with patch-based CNNs
2.5.1 Challenges in patch-based segmentation
2.5.2 Advantages of patch-based approaches
2.5.3 Comparisons with fully convolutional networks
2.5.4 Applications and developments
3 U-Net and variants for biomedical image segmentation
3.1 Introduction to U-Net architecture
3.1.1 Contracting path: encoder
3.1.2 Expansive path: decoder
3.1.3 Skip connections and their importance
3.1.4 Applications and impact
3.2 Extensions and improvements of U-Net
3.2.1 U-Net++: nested U-Net architecture
3.2.2 Attention U-Net: incorporating attention mechanisms
3.2.3 Multiscale U-Net: handling multiresolution inputs
3.2.4 Recurrent U-Net: adding recurrent layers
3.2.5 U-Net3D: toward the extension of U-Net for volumetric data
3.3 Training U-Net models efficiently
3.3.1 Data augmentation techniques
3.3.2 Optimization of loss functions
3.3.3 Regularization and hyperparameter tuning
3.3.4 Transfer learning and pretrained models
3.3.5 Computational resources and parallelization
3.4 Applications of U-Net beyond biomedical imaging
3.4.1 Satellite as well as aerial image segmentation
3.4.2 Self-driving cars and road environment understanding
3.4.3 Industrial and manufacturing applications
3.4.4 Robotics and drone uses
3.5 Challenges in U-Net architectures
3.5.1 Time and space complexity and time requirements
3.5.2 Overfitting and generalization issues
3.5.3 Handling variability in input data
3.5.4 Loss of fine details and spatial resolution
3.5.5 Scalability and adaptability
4 DeepLab family of architectures
4.1 Introduction to DeepLab architecture
4.1.1 Atrous convolutions and their impact
4.1.2 DeepLab's spatial pyramid pooling module
4.1.3 Advantages of DeepLab architecture
4.1.4 Evolution of DeepLab models
4.2 DeepLabv2
4.2.1 ASPP
4.2.2 Advantages over previous models
4.2.3 Implementation details and performance
4.2.4 Challenges and limitations
4.3 DeepLabv3
4.3.1 ASPP
4.3.2 Improved feature extraction with ASPP
4.3.3 Comparison with previous DeepLab models
4.3.4 Applications and performance
4.4 DeepLabv3+
4.4.1 Architecture and enhancements
4.4.2 ASPP improvement
4.4.3 Performance and benchmarking
4.4.4 Applications and use cases
4.5 Challenges and open issues in DeepLab models
4.5.1 Time complexity and inference time
4.5.2 Handling fine details and small objects
4.5.3 Generalization across diverse datasets
4.5.4 Scalability and multiresolution processing
5 Mask R-CNN and the rise of instance segmentation
5.1 Introduction to mask R-CNN
5.1.1 Architecture and major building blocks
5.1.2 Innovations in RoIAlign
5.1.3 Performance and benchmarking
5.1.4 Applications and impact
5.2 Technical innovations in mask R-CNN
5.2.1 ROIAlign to get better region proposals
5.2.2 Feature pyramid networks (FPNs) for multiscale feature learning
5.2.3 Improved mask prediction with dense predictions
5.2.4 Mask scoring and confidence estimation
5.3 Applications of mask R-CNN
5.3.1 Ordinary average intelligent self-driving cars and adaptive traffic control
5.3.2 Diagnostic imaging
5.3.3 Looks into industrial quality control and defect detection
5.3.4 Robotic manipulation and interaction
5.3.5 Video surveillance and security
5.4 Extensions and variants of mask R-CNN
5.4.1 PointRend: enhanced mask prediction
5.4.2 Mask scoring R-CNN: improving mask confidence
5.4.3 Hybrid task cascade (HTC): multistage detection enhancement
5.4.4 Efficient mask R-CNN: efficiency optimization for speed and resource consuming
5.4.5 Integration with transformers: utilizing the attention mechanisms
5.5 Limitations and challenges in instance segmentation
5.5.1 High computational overhead
5.5.2 Handling occlusions and overlapping objects
5.5.3 Scalability to large-scale datasets
5.5.4 Generalization across diverse domains
5.5.5 Annotation quality and data imbalance
6 Transformer-based architectures for image segmentation
6.1 Introduction to ViTs
6.1.1 Architecture and mechanism
6.1.2 Advantages over convolutional networks
6.1.3 Performance and benchmarking
6.2 Swin transformer
6.2.1 Secondary-level hires: architectural hierarchy and window-based demand
6.2.2 Advantages over previous transformers
6.2.3 Performance in image segmentation
6.2.4 Applications and use cases
6.3 SegFormer
6.3.1 Architecture and design principles
6.3.2 Advantages over previous models
6.3.3 Performance on benchmark datasets
6.3.4 Applications and practical use
6.4 Hybrid CNN-transformer architectures
6.4.1 Combination of CNN and transformer
6.4.2 Advantages of hybrid approaches
6.4.3 Hybrid architectures and their experience state with challenges
6.4.4 Design considerations and model variants
6.5 Challenges and future directions in transformer-based segmentation
6.5.1 Time and space complexity
6.5.2 Handling fine details and spatial resolution
6.5.3 Scalability and efficiency
6.5.4 Generalization to diverse domains
6.5.5 Training data and annotation requirements
7 Conclusion
References
Machine learning algorithm for medical image processing
1 Introduction
1.1 Overview of medical image processing
1.2 Key imaging modalities: PET and SPECT
2 Historical background of medical image processing
2.1 Early developments in medical imaging
2.2 The emergence of digital imaging
2.3 Milestones in image processing algorithms
2.3.1 Image filtering techniques for introductory courses
2.3.2 A review of newer image segmentation techniques
2.3.3 Improved methods of reconstruction
2.3.4 Incorporation of basics of machine learning and basics of deep learning
2.4 Key technological innovations
2.4.1 New developments in imaging hardware and sensor devices
2.4.2 Use of GPUs to support image processing endeavors
2.4.3 Implementation of multimodal imaging techniques
2.4.4 Real-time imaging system evolution
2.4.5 Burst of big data on the medical image analysis
2.5 Evolution of standards and protocols
2.5.1 Beginning of DICOM standard
2.5.2 The development of image quality assurance protocols
2.5.3 Importation of regulatory guidelines for implementation of medical images
2.5.4 Interoperability standards in healthcare - adoption
3 Current challenges in medical image analysis
3.1 Data heterogeneity
3.1.1 Effects of patients' characteristics
3.1.2 Manipulating differences in images resolution and its formats
3.2 Scalability and data volume
3.2.1 Managing a growing amount of imaging information
3.2.2 Problems in data storage and management for big data
3.2.3 Challenges in designing image processing algorithm for huge high-dimensional images
3.2.4 Ways in which big data affects the amount of computation required
3.3 Annotation and labeling
3.3.1 Lack of labeled medical image datasets
3.3.2 Overview of obstacles in gaining expert annotations
3.3.3 Why is it important to standardize annotation protocols?
3.3.4 Applying semisupervised and unsupervised learning
3.4 Interpretability of models
3.4.1 Introduction of explainable artificial intelligence or XAI
3.4.2 Statistics in model validation
3.4.3 Effects on clinical practice
3.4.4 Possible future developments with respect to increasing interpretable clarity
3.5 Regulatory and ethical concerns
3.5.1 Data privacy and security
3.5.2 Bias and fairness presented in machine learning models
3.5.3 Overview - guidelines and principles for the use of artificial intelligence-based tools
4 Key imaging modalities in medical image processing
4.1 X-ray imaging
4.1.1 Introduction to mechanisms of X-ray production and detection
4.1.2 Different methods for improving X-ray images
4.1.3 Constraints of X-ray imaging and their resolution
4.1.4 Application of machine learning in diagnosis using X-ray images
4.2 Computed tomography (CT)
4.2.1 Applications of computed tomography in clinical practice
4.2.2 Recent breakthrough in image processing: noise reduction and artifact removal
4.2.3 Methods of increasing the contrast of CT pictures
4.2.4 Artificial intelligence in automated computerized tomography pictures analysis
4.3 Magnetic resonance imaging (MRI)
4.3.1 MRI technology principles
4.3.2 Clinical applications of MRI
4.3.3 Advanced MRI techniques
4.3.4 Technique analysis of advanced imaging for MRI
4.3.5 Prospects and problems
4.4 Ultrasound imaging
4.4.1 Hands-on principles of ultrasound imaging
4.4.2 Sonography application in treating humans
4.4.3 Techniques used in image processing of ultrasound
4.4.4 Difficulties in analyzing ultrasound pictures
4.4.5 More innovations in ultrasound
4.5 Nuclear imaging (PET and SPECT)
4.5.1 Clinical uses and value of PET scanning
4.5.2 SPECT in clinical practice
4.5.3 Challenges in nuclear imaging
5 Fundamental concepts in medical image processing
5.1 Image acquisition
5.1.1 Principles of image formation
5.1.2 Role of sensors and detectors
5.1.3 Effectiveness of some parameters of acquisition on image quality
5.1.4 Methodologies for enhancing the process of image capture
5.1.5 Real-time image acquisition-developmental technology progress
5.2 Image enhancement
5.2.1 Noise reduction methods
5.2.2 Edge detection and sharpening
5.2.3 Histogram processing
5.2.4 Multimodal image restoration and enhancement
5.3 Image segmentation
5.3.1 Performance of the algorithm in enhancing segmentations
5.3.2 Uses of segmentation in disease diagnosis and treatment management
5.3.3 The difficulties and opportunities of medical image segmentation
5.4 Image registration
5.4.1 Basics of image registration
5.4.2 Methods of rigid registration
5.4.3 A review of nonrigid registration methods
5.5 Feature extraction and analysis
5.5.1 Using feature extraction in diagnosing and treating illness
6 Impact of medical image processing on clinical practice
6.1 Improving diagnostic accuracy
6.1.1 Improved visualization and detection
6.1.1 Enhancements in image segmentation and analysis
6.1.2 Prevention of minimization of diagnostic errors
6.1.3 Help identify the disease early on
6.1.4 Championing personalized medicine
6.2 Enhancing treatment planning
6.2.1 More detailed representation of anatomy of bodies
6.2.2 Accuracy in the use of radiation
6.2.3 Organ-specific surgery
6.2.4 Treatment plans for individual patients
6.2.5 Supervisory and reassessing interventions
6.2.6 Compatibility with other diagnostic instruments
6.2.7 Reduction of treatment-related risk
6.3 Monitoring disease progression
6.3.1 Monitoring the tumor burden and its reaction to treatment
6.3.2 Evaluating the disease course in neurodegenerative diseases
6.3.3 How to assess disease progress in autoimmune diseases
6.4 Personalized medicine and patient-specific treatment
6.4.1 Purpose of imaging in personalized treatment
6.4.2 Effect of website design on patient outcomes and healthcare systems
6.4.3 Current issues and future research
6.5 Telemedicine and remote diagnostics
6.5.1 Application of medical image processing in telemedicine
6.5.2 Improvement in and efficiency of diagnosis
6.5.3 Remote surveillance and subsequent contact
6.5.4 Issues and areas to think through concerning telemedicine and remote diagnostics
6.5.5 Future evolution in telemedicine and remote diagnosis
7 Conclusion
References
Machine learning models for predicting anomaly in scanned images
1 Introduction
2 Fundamentals of multimodal data integration
2.1 Introduction to multimodal data
2.2 Challenges in multimodal integration
2.3 Types of multimodal data
2.3.1 Image data
2.3.2 Text data
2.3.3 Sensor data
2.3.4 Audio data
2.3.5 Time-series data
2.3.6 Geographic and spatial data
2.4 Data fusion techniques
2.4.1 Feature-level fusion
2.4.2 Decision-level fusion
2.4.3 Model-level fusion
2.4.4 Graph-based fusion methods
2.4.5 Hybrid fusion approaches
2.5 Evaluation metrics for multimodal integration
2.5.1 Accuracy and precision
2.5.2 Recall and F1-score
2.5.3 Receiving operating characteristic curve (ROC-AUC)
2.5.4 Multimodal fusion metrics
3 Techniques for multimodal data fusion
3.1 Feature-level fusion
3.1.1 Methods for extracting features
3.1.2 Combining features from different modalities
3.1.3 Challenges in feature-level fusion
3.1.4 Applications and performance evaluation
3.2 Decision-level fusion
3.2.1 Methods of combining model outputs
3.2.2 Advantages of decision-level fusion
3.2.3 Challenges and considerations
3.2.4 Evaluation and performance metrics
3.3 Model-level fusion
3.3.1 Approaches to model-level fusion
3.3.2 Advantages of model-level fusion
3.3.3 Challenges and considerations
3.3.4 Evaluational and performance metrics
3.4 Deep learning approaches
3.4.1 Multi-input neural networks
3.4.2 Multimodal autoencoders
3.4.3 Cross-modal attention mechanisms
3.4.4 End-to-end multimodal networks
3.5 Graph-based fusion methods
3.5.1 Building and presenting the graph
3.5.2 Graph-based learning and analysis
3.5.3 Applications and benefits
3.5.4 Challenges and considerations
4 Application of multimodal anomaly detection in medical imaging
4.1 Combining imaging and clinical data
4.1.1 Data integration methods
4.1.2 Challenges in data fusion
4.1.3 Machine learning approaches for integration
4.1.4 Uses and advantages of clinical options
4.2 Fusion of MRI and PET scans
4.2.1 Integration methods for MRI and PET
4.2.2 Advantages of MRI and PET fusion
4.2.3 Difficulties in MRI and PET integration
4.2.4 End user and practice implications
4.3 Utilizing genomic data
4.3.1 Integrating genomics and imaging information
4.3.2 Improved anomaly detection with genomic data
4.3.3 Applications and case studies
4.4 Real-time monitoring systems
4.4.1 Integration of multimodal data streams
4.4.2 Clinical applications and benefits
4.4.3 Challenges and limitations
4.5 Case studies and applications
4.5.1 MRI and computed tomography (CT) scan
4.5.2 The use of PETs and genomic data
4.5.3 Interdisciplinary work: multiple varieties in cardiology
5 Industrial and manufacturing applications
5.1 Combining visual inspection with sensor data
5.1.1 Methods of integration
5.1.2 Advantages of the integrated techniques
5.1.3 Case studies and applications
5.2 Quality control systems
5.2.1 Real-time anomaly detection
5.3 Predictive maintenance
5.3.1 Data from body-worn sensors and plant operational records
5.3.2 Advanced data fusion techniques
5.4 Anomaly detection in smart factories
5.4.1 Energy and power saving
5.5 Case studies and implementation
5.5.1 Conformity in making automobiles
5.5.2 Predictive maintenance in the aerospace industry
5.5.3 Structural improvements for enhancing processes in semiconductor production
5.5.4 Real-time monitoring in food processing
5.5.5 Difficulties and prospectives
6 Challenges and solutions in multimodal data integration
6.1 Data synchronization and alignment
6.1.1 Challenges in data synchronization
6.1.2 Challenges in data alignment
6.1.3 Issues of data synchronization
6.1.4 Challenges in data alignment
6.2 Handling missing and incomplete data
6.2.1 Imputation techniques
6.2.2 Data augmentation
6.2.3 Data fusion strategies
6.2.4 Robustness and model adaptation
6.2.5 Evaluation and validation
6.3 Computational complexity
6.3.1 Issues of high-dimensional data
6.3.2 Scalability issues
6.3.3 Algorithmic complexity
6.3.4 Real-time processing needs
6.3.5 Solutions and strategies
6.4 Data privacy and security
6.4.1 Challenges in data privacy
6.4.2 Security risks and threats
6.4.3 Proposals for data protection and protection
6.4.4 Compliance with regulations
6.5 Scalability issues
6.5.1 Amount of data collected and subsequently, the required storage
6.5.2 Complexity of computations and the computational power
6.5.3 Data integration and analysis in real time
6.5.4 Interoperability and data standardization
7 Conclusion
References
Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data
1 Introduction
2 Integration of supervised and unsupervised learning models
2.1 Semisupervised learning approaches for anomaly detection
2.2 Autoencoders for unsupervised feature extraction in supervised models
2.3 Combining clustering with classification for anomaly detection
2.4 Enhancing robustness of anomaly detection through hybrid supervised and unsupervised learning
3 Ensemble learning approaches for anomaly detection
3.1 Random forests and gradient boosting for detecting visual anomalies
3.2 Boosting and bagging in anomaly detection systems
3.3 The role of diversity in ensemble-based anomaly detection
3.4 Optimizing ensemble models for imbalanced anomaly detection datasets
4 Hybrid deep learning architectures for complex anomaly detection
4.1 CNN and RNN hybrid models for spatiotemporal anomaly detection
4.2 Integrating convolutional neural networks with autoencoders for anomaly detection
4.3 Using GANs and CNNs for generative anomaly detection
4.4 Recurrent neural networks for sequential anomaly detection in visual data
4.5 Combining variational autoencoders and deep neural networks for robust anomaly detection
5 Reinforcement learning enhanced hybrid models
5.1 Reinforcement learning for adaptive anomaly detection
5.2 Exploring deep Q-learning in anomaly detection tasks
5.3 Combining reinforcement learning with autoencoders for dynamic anomaly detection
5.4 Policy gradient methods for online anomaly detection in visual data
5.5 Reinforcement learning for balancing precision and recall in anomaly detection
6 Hybrid models for multimodal anomaly detection
6.1 Multimodal learning approaches for integrated anomaly detection
6.2 Combining image, sensor, and metadata for improved anomaly detection
6.3 Cross-domain anomaly detection using hybrid neural networks
6.4 Multimodal fusion networks for enhanced anomaly detection accuracy
7 Conclusion
References
AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics
1 Introduction
2 Historical context of diagnostics and AI
2.1 Evolution of diagnostic techniques
2.2 Milestones in AI development
2.2.1 The work from 1950s to 1960s in the development of AI
2.2.2 The development of machine learning (1980s-1990s)
2.2.3 Third and fourth waves of AI development for modern AI (2000s-2010s)
2.2.4 Implementation of AI in healthcare applications (2010-present)
2.2.5 Explainability in artificial intelligence: a timeline (from 2020s)
2.3 Integration of AI with traditional diagnostics
2.4 Challenges in pre-AI diagnostics
2.5 Transition to AI-driven diagnostics
3 Fundamentals of artificial intelligence in healthcare
3.1 Overview of AI technologies
3.2 AI algorithms in diagnostics
3.3 AI's role in data processing
3.4 AI and decision-making in healthcare
3.5 Benefits of AI integration in diagnostics
4 Importance of diagnostic sensitivity and specificity
4.1 Defining sensitivity in diagnostics
4.2 Understanding specificity in diagnostics
4.3 AI's impact on sensitivity
4.4 AI's role in enhancing specificity
4.5 Challenges in achieving optimal sensitivity and specificity
5 AI-driven innovations in diagnostic materials
5.1 Biosensors and AI
5.2 Nanotechnology in AI diagnostics
5.3 Smart materials and AI
5.4 Lab-on-a-chip systems
5.5 AI and point-of-care diagnostics
6 Current trends and future prospects in AI-enhanced diagnostics
6.1 Emerging AI technologies
6.2 AI in personalized medicine
6.3 Global adoption of AI in diagnostics
6.4 Ethical considerations in AI diagnostics
6.5 Future directions
7 Conclusion
References
Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging
1 Introduction
1.1 Quantum dots in medical imaging
1.2 VARCHAR synthesis and functionalization of quantum dots
1.3 Optical properties and performance metrics
1.4 Cost and commercialization barriers
1.5 Future directions and research opportunities
2 Definition and characteristics of quantum dots
2.1 Definition and basic concept
2.2 Size and shape dependence
2.3 Optical properties (e.g., fluorescence and quantum yield)
2.3.1 Fluorescence
2.3.2 Quantum yield
2.3.3 Photostability
2.3.4 Emission and excitation spectra
2.4 Electronic properties and quantum confinement
2.5 Comparison with traditional fluorescent materials
2.5.1 Size-tunable emission
2.5.2 Photostability and brightness
2.5.3 Quantum yield and efficiency
2.5.4 Spectral range and overlap
2.5.5 Chemical stability and functionalization
3 Materials and synthesis methods
3.1 Semiconductor materials used (e.g., CdSe, CdTe, and Si)
3.1.1 Cadmium selenide (CdSe)
3.1.2 Cadmium telluride (CdTe)
3.1.3 Silicon (Si)
3.1.4 Zinc oxide (ZnO) and zinc sulfide (ZnS)
3.2 Colloidal synthesis techniques
3.2.1 Hot-injection method
3.2.2 Solvothermal synthesis
3.2.3 Hydrothermal synthesis
3.2.4 Reverse micelle method
3.2.5 CVD technique
3.3 Chemical vapor deposition
3.3.1 Growth of gallium arsine as a foundation of chemical vapor deposition
3.3.2 Chemical vapor deposition (CVD) advantages
3.3.3 Challenges and considerations
3.4 Molecular beam epitaxy
3.4.1 One of the principles of molecular beam epitaxy
3.4.2 Growth control and precision
3.5 Pros and cons of different synthesis methods
3.5.1 Colloidal synthesis
3.5.2 CVD
3.5.3 MBE
3.5.4 Laser ablation
4 Quantum dot structures and variants
4.1 Core-shell quantum dots
4.1.1 Structure and composition
4.1.2 Drawbacks of nano-core-shell structures
4.1.3 Synthesis methods
4.1.4 Applications
4.2 Core-shell-shell quantum dots
4.2.1 Enhanced optical properties
4.2.2 Reinforcement of the building structure and better structural stability
4.2.3 Specificity of functionalization and flexibility
4.3 Dot-in-rod and dot-in-matrix structures
4.3.1 Dot-in-rod structures
4.3.2 Dot-in-matrix structures
4.3.3 Pros
4.4 Multicolor and multimodal quantum dots
4.4.1 Multicolor quantum dots
4.4.2 Multimodal quantum dots
4.5 Functionalized and surface-coated quantum dots
4.5.1 Types of surface coatings
4.5.2 Challenges and considerations
5 Optical properties and performance metrics
5.1 Emission spectra and color tuning
5.1.1 Color tuning techniques
5.1.2 Performance metrics
5.2 Photostability and brightness
5.2.1 Photostability
5.2.2 Brightness
5.3 Quantum yield and efficiency
5.3.1 Quantum yield
5.3.2 Photoluminescence efficiency
5.3.3 Nonradiative processes
5.3.4 Measurement techniques
5.4 Excitation and emission wavelengths
5.4.1 Wavelength dependence on size
5.4.2 Excitation wavelengths
5.4.3 Emission wavelengths
5.4.4 Spectral range and resolution
5.4.5 Influence of surface coatings
5.4.6 Practical considerations
5.5 Influence of size and surface modification on optical properties
5.5.1 Size-dependent optical properties
5.5.2 Surface modification effects
5.5.3 Performance metrics for optimization
6 Challenges in quantum dot development
6.1 Toxicity and environmental concerns
6.1.1 Toxicity of quantum dots
6.1.2 Environmental impact
6.1.3 Mitigation strategies
6.2 Stability and reproducibility issues
6.2.1 Chemical stability
6.2.2 Photostability
6.2.3 Reproducibility of synthesis
6.2.4 Storage and handling
6.3 Scalability of synthesis methods
6.3.1 Colloidal synthesis
6.3.2 CVD
6.3.3 MBE
6.3.4 Hydrothermal and solvothermal synthesis
6.3.5 Laser ablation
6.4 Functionalization challenges
6.4.1 Stability of functional groups
6.4.2 Efficient coupling and reaction conditions
6.4.3 Changes in the characteristics of quantum dots
6.4.4 Cost and complexity
6.5 Cost and commercialization barriers
6.5.1 High production costs
6.5.2 Economic viability
6.5.3 Commercialization challenges
7 Conclusion
References
Machine learning application in tissue engineering: scaffold design
1 Introduction
2 Data acquisition and preprocessing
2.1 Sources of scaffold material data
2.2 Techniques for data preprocessing in scaffold design
2.3 Handling missing data
2.4 Standardization and normalization of data
2.5 Quality assessment and validation of datasets
3 Feature selection and engineering
3.1 Identification of relevant features in scaffold design
3.2 Methods for feature selection in machine learning models
3.3 Incorporating domain knowledge into feature engineering
3.4 Strategies for dimensionality reduction
3.5 Balancing computational efficiency with feature richness
4 Model selection and evaluation
4.1 Comparison of machine learning algorithms for scaffold design
4.2 Cross-validation techniques for model evaluation
4.3 Metrics for assessing model performance (e.g., accuracy, precision, and recall)
4.4 Strategies for addressing overfitting and underfitting
4.5 Interpretability of machine learning models in scaffold design
5 Optimization techniques
5.1 Overview of optimization algorithms used in scaffold design
5.2 Gradient-based optimization methods
5.3 Evolutionary algorithms for scaffold optimization
5.4 Hyperparameter tuning for machine learning models
5.5 Ensemble learning approaches for improving optimization results
6 Predictive modeling case studies
6.1 Application of predictive modeling in scaffold material selection
6.2 Case study: predicting mechanical properties of scaffolds using machine learning
6.3 Case study: predictive modeling for scaffold degradation kinetics
6.4 Case study: Machine learning approaches for predicting scaffold-cell interactions
6.5 Case study: Integrating predictive modeling into scaffold fabrication processes
7 Conclusion
References
Machine learning approaches to improve electrospun nanofibers' performance and properties for medical applications
1 Introduction
2 Introduction to electrospinning and nanofiber fabrication
2.1 History and development of electrospinning technology
2.2 Basic principles of electrospinning
2.3 Types of polymers used in electrospinning for biomedical applications
2.4 Parameters affecting nanofiber morphology and properties
2.5 Advances in electrospinning techniques
3 Properties of electrospun nanofibers
3.1 High surface-area-to-volume ratio and its biomedical significance
3.2 Mechanical properties
3.3 Porosity and fiber diameter
3.4 Biodegradability and biocompatibility of nanofibers
3.5 Functionalization of nanofibers
4 Electrospun nanofibers in drug delivery systems
4.1 Nanofiber-based platforms for sustained and controlled drug release
4.2 Drug encapsulation methods
4.3 Nanofibers for targeted drug delivery
4.4 Electrospun nanofibers in transdermal drug delivery
4.5 Role of nanofibers in cancer therapy and chemotherapy drug delivery
5 Electrospun nanofibers in tissue engineering
5.1 Nanofiber scaffolds
5.2 Applications in bone tissue engineering
5.3 Use of nanofibers in cartilage and ligament regeneration
5.4 Electrospun nanofibers for nerve tissue repair
5.5 Enhancing stem cell differentiation through nanofiber scaffolds
6 Wound healing applications of electrospun nanofibers
6.1 Nanofiber dressings for enhanced healing and moisture retention
6.2 Antimicrobial electrospun nanofibers for infection control
6.3 Incorporating growth factors in nanofibers to accelerate healing
6.4 Nanofibers for chronic wounds and diabetic ulcers
6.5 Biodegradable nanofibers for scar-free healing
7 Conclusion
References
Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds
1 Introduction
2 Standards for data collection and reporting
2.1 Development of standardized protocols for biodegradable scaffold data collection
2.2 Best practices for documenting scaffold properties and environmental conditions
2.3 Methods for ensuring consistency in degradation rate measurements
2.4 Techniques for quality control in experimental data
2.5 Recommendations for reporting and sharing data in scaffold research
3 Data sources and types
3.1 Overview of experimental data types for scaffold stability assessment
3.2 Integration of clinical data with laboratory results
3.3 Utilization of computational simulations and modeling data
3.4 Collection and use of real-world performance data from clinical trials
3.5 Role of longitudinal studies in data collection for scaffold degradation
4 Data integration methods
4.1 Techniques for merging experimental and computational data
4.2 Approaches to integrating multisource data for predictive modeling
4.3 Data fusion strategies for enhancing scaffold stability predictions
4.4 Handling inconsistent data from various experimental platforms
4.5 Tools and platforms for efficient data integration and management
5 Data preprocessing and cleaning
5.1 Methods for handling missing or incomplete data in scaffold studies
5.2 Techniques for data normalization and standardization
5.3 Addressing data outliers and anomalies in scaffold research
5.4 Strategies for removing noise from experimental data
5.5 Tools and software for data cleaning and preprocessing
6 Data validation and quality assurance
6.1 Protocols for validating experimental data accuracy and reliability
6.2 Techniques for cross-validating data from different sources
6.3 Quality assurance practices in scaffold stability studies
6.4 Methods for assessing data precision and consistency
6.5 Evaluating the impact of data quality on predictive model performance
7 Conclusion
References
Customization of medical implants using 3D printing
1 Introduction
1.1 Active uses in healthcare
1.2 Benefits of enhanced surgical outcomes
2 Historical development of 3D printing technologies
2.1 Origins and evolution of additive manufacturing
2.2 Early applications of 3D printing in medicine
2.3 Milestones in 3D printing technology development
2.3.1 Stereolithography of three-dimensional objects with the aid of light (1986)
2.3.2 Patent of the fused deposition modeling (1988)
2.3.3 Select laser sintering development (1989)
2.3.4 Emerging technique: direct metal laser sintering (1995)
2.3.5 Bioprinters outlined: challenges of bioprinting (2003)
2.3.6 Multimaterial and color printing, multimaterial printing, advanced color printing (2009)
2.3.7 High-resolution 3D printing, which was released to the public in 2014
2.4 Comparative analysis of early and modern 3D printing techniques
2.4.1 Early techniques: The present and future role of 3D printing
2.4.2 Modern techniques: advancements and innovations
2.4.3 Comparative insights
2.5 Pioneering studies and breakthroughs in medical 3D printing
3 Basic principles of 3D printing
3.1 Fundamentals of additive manufacturing processes
3.2 Layer-by-layer construction
3.3 Common 3D printing techniques and their mechanisms
3.3.1 FDM
3.3.2 SLA
3.3.3 SLS
3.3.4 DMLS
3.3.5 Digital light processing and digital micromirror device (DMD)
3.3.6 Inkjet printing
3.4 Materials used in 3D printing and their properties
3.5 Understanding the CAD-to-print workflow
4 Current applications of 3D printing in medicine
4.1 Customized implants and prosthetics
4.2 3D printing in surgical planning and simulation
4.3 Bioprinting
4.4 Development of medical instruments and tools
4.5 Educational models and training simulators
5 Benefits of 3D printing in medical applications
5.1 Enhanced personalization and patient outcomes
5.2 Reduced production time and costs
5.3 Increased accuracy and precision in medical devices
5.4 Innovation in complex and custom designs
5.5 Improved surgical outcomes through preoperative planning
6 Challenges and limitations of 3D printing in medicine
6.1 Material limitations and biocompatibility issues
6.2 Regulatory and safety concerns
6.3 Cost and accessibility of 3D printing technologies
6.4 Technical challenges in printing complex structures
6.5 Integration with existing medical practices and systems
7 Conclusion
References
Index
De Gruyter Series in Advanced Mechanical Engineering
Already published in the series
Contents
List of contributors
Machine learning models for predicting anomaly in scanned images
1 Introduction
2 Fundamentals of multimodal data integration
2.1 Introduction to multimodal data
2.2 Challenges in multimodal integration
2.3 Types of multimodal data
2.3.1 Image data
2.3.2 Text data
2.3.3 Sensor data
2.3.4 Audio data
2.3.5 Time-series data
2.3.6 Geographic and spatial data
2.4 Data fusion techniques
2.4.1 Feature-level fusion
2.4.2 Decision-level fusion
2.4.3 Model-level fusion
2.4.4 Graph-based fusion methods
2.4.5 Hybrid fusion approaches
2.5 Evaluation metrics for multimodal integration
2.5.1 Accuracy and precision
2.5.2 Recall and F1-score
2.5.3 Receiving operating characteristic curve (ROC-AUC)
2.5.4 Multimodal fusion metrics
3 Techniques for multimodal data fusion
3.1 Feature-level fusion
3.1.1 Methods for extracting features
3.1.2 Combining features from different modalities
3.1.3 Challenges in feature-level fusion
3.1.4 Applications and performance evaluation
3.2 Decision-level fusion
3.2.1 Methods of combining model outputs
3.2.2 Advantages of decision-level fusion
3.2.3 Challenges and considerations
3.2.4 Evaluation and performance metrics
3.3 Model-level fusion
3.3.1 Approaches to model-level fusion
3.3.2 Advantages of model-level fusion
3.3.3 Challenges and considerations
3.3.4 Evaluational and performance metrics
3.4 Deep learning approaches
3.4.1 Multi-input neural networks
3.4.2 Multimodal autoencoders
3.4.3 Cross-modal attention mechanisms
3.4.4 End-to-end multimodal networks
3.5 Graph-based fusion methods
3.5.1 Building and presenting the graph
3.5.2 Graph-based learning and analysis
3.5.3 Applications and benefits
3.5.4 Challenges and considerations
4 Application of multimodal anomaly detection in medical imaging
4.1 Combining imaging and clinical data
4.1.1 Data integration methods
4.1.2 Challenges in data fusion
4.1.3 Machine learning approaches for integration
4.1.4 Uses and advantages of clinical options
4.2 Fusion of MRI and PET scans
4.2.1 Integration methods for MRI and PET
4.2.2 Advantages of MRI and PET fusion
4.2.3 Difficulties in MRI and PET integration
4.2.4 End user and practice implications
4.3 Utilizing genomic data
4.3.1 Integrating genomics and imaging information
4.3.2 Improved anomaly detection with genomic data
4.3.3 Applications and case studies
4.4 Real-time monitoring systems
4.4.1 Integration of multimodal data streams
4.4.2 Clinical applications and benefits
4.4.3 Challenges and limitations
4.5 Case studies and applications
4.5.1 MRI and computed tomography (CT) scan
4.5.2 The use of PETs and genomic data
4.5.3 Interdisciplinary work: multiple varieties in cardiology
5 Industrial and manufacturing applications
5.1 Combining visual inspection with sensor data
5.1.1 Methods of integration
5.1.2 Advantages of the integrated techniques
5.1.3 Case studies and applications
5.2 Quality control systems
5.2.1 Real-time anomaly detection
5.3 Predictive maintenance
5.3.1 Data from body-worn sensors and plant operational records
5.3.2 Advanced data fusion techniques
5.4 Anomaly detection in smart factories
5.4.1 Energy and power saving
5.5 Case studies and implementation
5.5.1 Conformity in making automobiles
5.5.2 Predictive maintenance in the aerospace industry
5.5.3 Structural improvements for enhancing processes in semiconductor production
5.5.4 Real-time monitoring in food processing
5.5.5 Difficulties and prospectives
6 Challenges and solutions in multimodal data integration
6.1 Data synchronization and alignment
6.1.1 Challenges in data synchronization
6.1.2 Challenges in data alignment
6.1.3 Issues of data synchronization
6.1.4 Challenges in data alignment
6.2 Handling missing and incomplete data
6.2.1 Imputation techniques
6.2.2 Data augmentation
6.2.3 Data fusion strategies
6.2.4 Robustness and model adaptation
6.2.5 Evaluation and validation
6.3 Computational complexity
6.3.1 Issues of high-dimensional data
6.3.2 Scalability issues
6.3.3 Algorithmic complexity
6.3.4 Real-time processing needs
6.3.5 Solutions and strategies
6.4 Data privacy and security
6.4.1 Challenges in data privacy
6.4.2 Security risks and threats
6.4.3 Proposals for data protection and protection
6.4.4 Compliance with regulations
6.5 Scalability issues
6.5.1 Amount of data collected and subsequently, the required storage
6.5.2 Complexity of computations and the computational power
6.5.3 Data integration and analysis in real time
6.5.4 Interoperability and data standardization
7 Conclusion
References

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