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Record Nr. |
UNINA9910627260103321 |
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Titolo |
Artificial intelligence and machine learning for healthcare : image and data analytics / / edited by Chee Peng Lim, [and four others] |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2023] |
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©2023 |
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ISBN |
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Descrizione fisica |
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1 online resource (239 pages) |
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Collana |
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Intelligent Systems Reference Library ; ; v.228 |
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Disciplina |
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Soggetti |
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Artificial intelligence - Medical applications |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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 |
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