06940nam 2200457 450 991062726010332120230225101534.03-031-11154-0(CKB)5840000000091637(MiAaPQ)EBC7102068(Au-PeEL)EBL7102068(PPN)264955161(EXLCZ)99584000000009163720230225d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial intelligence and machine learning for healthcare image and data analytics /edited by Chee Peng Lim, [and four others]Cham, Switzerland :Springer,[2023]©20231 online resource (239 pages)Intelligent Systems Reference Library ;v.2283-031-11153-2 Includes bibliographical references.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.Intelligent Systems Reference LibraryArtificial intelligenceMedical applicationsArtificial intelligenceMedical applications.060Chee Peng LimMiAaPQMiAaPQMiAaPQBOOK9910627260103321Artificial intelligence and machine learning for healthcare3027984UNINA02926nam 2200685Ia 450 991081146040332120200520144314.01-135-22975-91-135-22976-71-282-44417-497866124441730-203-09263-510.4324/9780203092637 (CKB)1000000000811376(EBL)455480(OCoLC)609844540(Au-PeEL)EBL455480(CaPaEBR)ebr10358650(CaONFJC)MIL244417(OCoLC)742296894(OCoLC)893194508(OCoLC)1059568957(FINmELB)ELB149184(MiAaPQ)EBC455480(EXLCZ)99100000000081137620780525e20101976 uy 1engur|n|---|||||txtrdacontentcrdamediacrrdacarrierBad newsVolume 1 /Glasgow University Media Group ; [written by Peter Beharrell ... et al.] ; foreword by Richard Hoggart1st ed.Abingdon ;New York Routledge2010,c19761 online resource (331 p.)Routledge revivalsOriginally published in 1976.0-415-56787-4 0-415-56376-3 Includes bibliographical references and index.BOOK COVER; TITLE_01; COPYRIGHT_01; COPYRIGHT_02; TITLE_02; COPYRIGHT_03; CONTENTS; FOREWORD; ACKNOWLEDGEMENTS; 1 REVIEWING THE NEWS; 2 CONSTRUCTING THE PROJECT; 3 INSIDE THE TELEVISION NEWSROOM; 4 MEASURE FOR EASURE; 5 CONTOURS OF COVERAGE; 6 TRADES UINIONS AND THE MEDIA; 7 DOWN TO CASES; APPENDIX 1; APPENDIX 2; NOTES; INDEXIt is a commonly held belief that television news in Britain, on whatever channel, is more objective, more trustworthy, more neutral than press reporting. The illusion is exploded in this controversial study by the Glasgow University Media Group, originally published in 1976.The authors undertook an exhaustive monitoring of all television broadcasts over 6 months, from January to June 1975, with particular focus upon industrial news broadcasts, the TUC, strikes and industrial action, business and economic affairs.Their analysis showed how television news favours certain indiviRoutledge revivals.Television broadcasting of newsGreat BritainBroadcast journalismGreat BritainJournalismObjectivityTelevision broadcasting of newsBroadcast journalismJournalismObjectivity.070.19384.554Beharrell Peter1753871Hoggart Richard126775MiAaPQMiAaPQMiAaPQBOOK9910811460403321Bad news4189929UNINA