Artificial intelligence and machine learning for healthcare : image and data analytics / / edited by Chee Peng Lim, [and four others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2023]; ©2023
• Descrizione fisica: 1 online resource (239 pages)
• Disciplina: 060
• Soggetto topico: Artificial intelligence - Medical applications
• Persona (resp. second.): CheePeng Lim
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- 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.
• Titolo autorizzato: Artificial intelligence and machine learning for healthcare
• ISBN: 3-031-11154-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910627260103321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent Systems Reference Library