Artificial intelligence (AI) and machine learning (ML) have transformed many standard and conventional methods in undertaking health and well-being issues of humans. AL/ML-based systems and tools play a critical role in this digital and big data era to address a variety of medical and healthcare problems, improving treatments and quality of care for patients.
This edition on AI and ML for healthcare consists of two volumes. The first presents selected AI and ML studies on medical imaging and healthcare data analytics, while the second unveils emerging methodologies and trends in AI and ML for delivering better medical treatments and healthcare services in the future.
In this first volume, progresses in AI and ML technologies for medical image, video, and signal processing as well as health information and data analytics are presented. These selected studies offer readers theoretical and practical knowledge and ideas pertaining to recent advances in AI and ML for effective and efficient image and data analytics, leading to state-of-the-art AI and ML technologies for advancing the healthcare sector.
Author(s): Chee-Peng Lim, Ashlesha Vaidya, Yen-Wei Chen, Tejasvi Jain, Lakhmi C. Jain
Series: Intelligent Systems Reference Library, 228
Publisher: Springer
Year: 2022
Language: English
Pages: 238
City: Cham
Preface
Contents
1 An Introduction to Artificial Intelligence in Healthcare
1.1 Introduction to Artificial Intelligence
1.2 Artificial Intelligence in Healthcare
1.2.1 Natural Language Processing (NLP) Technology
1.2.2 Machine Learning (ML) Algorithms
1.2.3 Artificial Neural Networks
1.2.4 Bayesian Classifier
1.2.5 Classification/Decision Trees. Random Forest
1.2.6 Survival Regression Models
1.2.7 Cluster Analysis
1.3 Advantages of Artificial Intelligence in Healthcare
1.4 Limitations of Artificial Intelligence in Healthcare
1.5 Successful Applications of Artificial Intelligence in Healthcare
1.6 Conclusions
Appendix
Books
2 Radiomics: Approach to Precision Medicine
2.1 Introduction
2.2 Materials and Methods
2.2.1 Building of a Database
2.2.2 Segmentation of Target Volume
2.2.3 Extraction and Selection of Useful Radiomics Features
2.2.4 Model Building Based on Machine Learning Technologies
2.3 Results and Discussion
2.4 Conclusions
References
3 Artificial Intelligence Based Strategies for Data-Driven Radial MRI
3.1 Introduction
3.2 Related Work
3.2.1 Sparse Sampling Strategies
3.2.2 Contribution of the Manuscript
3.3 Problem Statement and Framework Description
3.3.1 Relationship Between Radial Projections and Image
3.3.2 Image Reconstruction, Resolution and Noise
3.3.3 Super-Resolution
3.3.4 Framework Details
3.3.5 Noise Threshold upper TT
3.4 Results and Discussion
3.5 Conclusion
References
4 Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images
4.1 Introduction
4.1.1 Domain-Shift Problem
4.1.2 Domain Adaptation
4.2 Domain Adaptation Using Adversarial Learning
4.2.1 Anchor-free Detector
4.2.2 Proposed Multi-phase Domain Adaptation Framework Using Adversarial Domain Classification Loss
4.3 Proposed Multi-phase Domain Adaptation Framework Using Adversarial Learning with Maximum Square Loss
4.3.1 Maximum Square Loss
4.3.2 Overall Framework with Adversarial Domain Classification and Maximum Square Loss
4.4 Experiments
4.4.1 Implementation Details
4.4.2 Dataset
4.4.3 Evaluation
4.4.4 Results
4.5 Conclusions
References
5 Multi-stage Synthetic Image Generation for the Semantic Segmentation of Medical Images
5.1 Introduction
5.2 Related Works
5.2.1 Synthetic Image Generation
5.2.2 Image-to-Image Translation
5.2.3 Retinal Image Synthesis and Segmentation
5.2.4 Chest X-ray Image Synthesis and Segmentation
5.3 Multi-stage Image Synthesis
5.3.1 Image Generation
5.4 Evaluation of Multi-stage Methods
5.4.1 Datasets
5.4.2 Segmentation Network
5.4.3 Experimental Setup
5.4.4 Two-Stage Method Evaluation
5.4.5 Three-Stage Method Evaluation
5.5 Conclusions
References
6 Classification of Arrhythmia Signals Using Hybrid Convolutional Neural Network (CNN) Model
6.1 Introduction
6.2 Literature Review
6.3 Methodology
6.4 Results and Discussion
6.5 Conclusions
Appendix 1
Appendix 2
Appendix 3
References
7 Polyp Segmentation with Deep Ensembles and Data Augmentation
7.1 Introduction
7.2 Related Methods
7.2.1 Overview of the Propose System
7.2.2 Loss Functions
7.3 Data Augmentation
7.3.1 Shadows
7.3.2 Contrast and Motion Blur
7.3.3 Color Mapping
7.4 Experimental Results
7.4.1 Data and Testing Protocol
7.4.2 Experiments
7.5 Conclusions
References
8 Autistic Verbal Behavior Parameters
8.1 Introduction
8.2 Estate of the Art
8.3 Proposal, Materials and Methods
8.4 Testing Protocol
8.5 Analysis of Tests
8.6 Conclusions and Future Work
References
9 Advances in Modelling Hospital Medical Wards
9.1 Introduction and Problem Addressed
9.2 Case Study and Data Analysis
9.3 Methodology and Results
9.4 Conclusion
References
10 Tracking Person-Centred Care Experiences Alongside Other Success Measures in Hearing Rehabilitation
10.1 Person-Centred Care in Research and Practice
10.1.1 Situated Action—Understanding the Context as a Basis for Meaningful Measures
10.1.2 Situated AI for Achieving High-Quality Person-Centred Care
10.2 Co-design for Person-Centred Care Measures
10.2.1 Co-design of Evaluation Instruments
10.2.2 Artificial Intelligence and PCC
10.3 Case Study: Co-creation of PCC Measures and Dashboard with Hearing Rehabilitation Provider
10.3.1 Method
10.4 Results
10.4.1 Stakeholder Workshops—Development of Tools
10.4.2 Stakeholder Feedback
10.4.3 Piloting the Dashboard
10.5 Discussion
10.5.1 Summary of Case Study
10.5.2 Discussion on Opportunities and Challenges for AI
10.5.3 Quality of Data
10.6 Conclusions
References
11 BioGNN: How Graph Neural Networks Can Solve Biological Problems
11.1 Overview of the Research Area
11.1.1 Biological Problems on Graphs
11.1.2 Deep Learning Models for Biological Graphs
11.2 Graph Neural Networks
11.2.1 The Graph Neural Network Model
11.2.2 Composite Graph Neural Networks
11.2.3 Layered Graph Neural Networks
11.2.4 Approximation Power of Graph Neural Networks
11.2.5 Software Implementation
11.3 Biological Applications
11.3.1 Prediction of Protein-Protein Interfaces
11.3.2 Drug Side-Effect Prediction
11.3.3 Molecular Graph Generation
11.4 Conclusions and Future Perspectives
References
