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[263]-276) and index. 327 $aDeep culture and democratic renewal -- Quandaries of individualism -- The justice of privilege -- Self-made men and women -- In America, all religions are true -- Ethnic ties that bind (loosely) -- Saving ourselves from materialism -- Venues for reflective democracy. 330 $aAmerica was built on stories: tales of grateful immigrants arriving at Ellis Island, Horatio Alger-style transformations, self-made men, and the Protestant work ethic. In this new book, renowned sociologist Robert Wuthnow examines these most American of stories--narratives about individualism, immigration, success, religion, and ethnicity--through the eyes of recent immigrants. In doing so, he demonstrates how the "American mythos" has both legitimized American society and prevented it from fully realizing its ideals. This magisterial work is a reflection and meditation on the national consciousness. It details how Americans have traditionally relied on narratives to address what it means to be strong, morally responsible individuals and to explain why some people are more successful than others--in short, to help us make sense of our lives. But it argues that these narratives have done little to help us confront new challenges. We pass laws to end racial discrimination, yet lack the resolve to create a more equitable society. We welcome the idea of pluralism in religion and values, yet we are shaken by the difficulties immigration presents. We champion prosperity for all, but live in a country where families are still experiencing homelessness. American Mythos aptly documents this disconnect between the stories we tell and the reality we face. Examining how cultural narratives may not, and often do not, reflect the reality of today's society, it challenges readers to become more reflective about what it means to live up to the American ideal. 606 $aSocial values$zUnited States 606 $aSocial ethics$zUnited States 606 $aImmigrants$zUnited States 607 $aUnited States$xMoral conditions 615 0$aSocial values 615 0$aSocial ethics 615 0$aImmigrants 676 $a303.3/72/0973 700 $aWuthnow$b Robert$0882687 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910345143703321 996 $aAmerican mythos$92473729 997 $aUNINA LEADER 10782nam 22006255 450 001 9910580165603321 005 20251113201157.0 010 $a3-031-00119-2 024 7 $a10.1007/978-3-031-00119-2 035 $a(MiAaPQ)EBC7021196 035 $a(Au-PeEL)EBL7021196 035 $a(CKB)23976934800041 035 $aEBL7021196 035 $a(AU-PeEL)EBL7021196 035 $a(PPN)269150749 035 $a(OCoLC)1333147806 035 $a(DE-He213)978-3-031-00119-2 035 $a(EXLCZ)9923976934800041 100 $a20220622d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging /$fedited by Patrick Veit-Haibach, Ken Herrmann 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (216 pages) 225 1 $aMedicine Series 300 $aDescription based upon print version of record. 311 08$aPrint version: Veit-Haibach, Patrick Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging Cham : Springer International Publishing AG,c2022 9783031001185 327 $aIntro -- Foreword -- Preface: Benefits and Challenges of AI/ML in Hybrid Imaging and Molecular Imaging -- Contents -- Part I: Technology -- 1: Role and Influence of Artificial Intelligence in Healthcare, Hybrid Imaging, and Molecular Imaging -- 1.1 AI Applications Support the Infrastructure and Interventions of Healthcare, Including Molecular Imaging -- 1.1.1 Drug Development -- 1.1.2 Clinical Workflow -- 1.2 AI's Clinical Applications with a Focus on Molecular Imaging -- 1.2.1 Understanding Disease -- 1.2.2 Diagnosis -- 1.2.3 Radiologic-Pathology Correlation -- 1.2.4 Characterization -- 1.2.5 Treatment Planning -- 1.2.6 Prediction of Response to Treatment -- 1.2.7 Overall Prognosis -- 1.2.8 Reporting -- 1.3 Conclusion -- References -- 2: Introduction to Machine Learning: Definitions and Hybrid Imaging Applications -- 2.1 Introduction -- 2.2 History and Basic Definitions -- 2.3 Learning Paradigms -- 2.4 General Concepts of Machine Learning Methods -- 2.5 Classical Machine Learning Approaches -- 2.6 Artificial Neural Networks -- 2.7 Radiomics and Radiogenomics -- 2.8 Imaging Applications -- 2.9 Conclusions and Perspectives -- References -- 3: Radiomics in Nuclear Medicine, Robustness, Reproducibility, and Standardization -- 3.1 Introduction -- 3.2 Robustness of Radiomic Features -- 3.3 Image Acquisition -- 3.4 Image Reconstruction -- 3.5 Segmentation -- 3.6 Image Processing -- 3.7 Discretization -- 3.8 Software -- 3.9 Pitfalls -- 3.10 Standardization -- 3.11 Discussion -- 3.12 Conclusion -- References -- 4: Evolution of AI in Medical Imaging -- 4.1 Disease Characterization -- 4.2 Segmentation -- 4.3 Image Generation/Reconstruction -- 4.4 Data Corrections -- 4.5 Image Registration -- 4.6 Radiology Reporting -- 4.7 Conclusion -- References -- 5: The Basic Principles of Machine Learning -- 5.1 Introduction. 327 $a5.1.1 The Task of ML -- 5.1.1.1 A Question -- 5.1.1.2 A Computer -- 5.1.1.3 An Algorithm or Model -- 5.1.1.4 Data to Interpret -- 5.1.2 Supervised Learning -- 5.1.3 Unsupervised Learning -- 5.1.4 Radiomics and Texture Analysis -- 5.1.5 Feature Reduction -- 5.1.6 Scaling and Normalization -- 5.1.7 Training, Validation, and Testing -- 5.2 Linear Regression -- 5.2.1 Under- and Overfitting -- 5.2.2 Linear Regression Mathematics -- 5.2.3 The Neural Network -- 5.2.4 The Objective Function -- 5.2.5 Gradient Descent -- 5.2.6 Deep Learning with Convolutional Neural Networks -- 5.2.7 Advanced Deep Learning Architectures -- 5.2.7.1 Autoencoders -- 5.2.7.2 ResNet -- 5.2.7.3 U-Net -- 5.2.7.4 Generative Adversarial Networks -- 5.2.7.5 Deep Boltzmann Machines -- 5.2.8 Deep Learning in Medical Image Analysis -- 5.2.8.1 Classification, Localization and Detection -- 5.2.8.2 Segmentation -- 5.2.8.3 Registration -- 5.2.8.4 Image Synthesis -- 5.2.9 Federated Learning -- References -- Part II: Clinical Applications -- 6: Imaging Biomarkers and Their Meaning for Molecular Imaging -- 6.1 Introduction -- 6.2 Imaging Biomarkers, Paradigm Shift in Medical Imaging -- 6.3 Imaging Biomarkers in Hybrid Molecular Imaging -- References -- 7: Integration of Artificial Intelligence, Machine Learning, and Deep Learning into Clinically Routine Molecular Imaging -- 7.1 Introduction -- 7.2 Classification -- 7.3 Segmentation -- 7.4 Detection and Localization -- 7.5 Applications of ML and DL in Molecular Imaging -- 7.6 Internal Department Applications -- 7.7 A Glance at Tomorrow -- 7.8 Workforce -- Redundancy, Displacement, Transformation, and Opportunity -- 7.9 Summary -- References -- 8: Imaging Biobanks for Molecular Imaging: How to Integrate ML/AI into Our Databases -- 8.1 Introduction -- 8.2 Imaging Biobanks in Molecular Imaging. 327 $a8.3 Bioethical Issues -- 8.4 Proposed Architecture -- References -- 9: Artificial Intelligence/Machine Learning in Nuclear Medicine -- 9.1 Introduction -- 9.2 Classification -- 9.2.1 Alzheimer's Disease -- 9.2.2 Parkinson's Disease -- 9.3 Segmentation -- 9.4 Image Generation and Processing -- 9.5 Low-Dose Imaging -- References -- 10: AI/ML Imaging Applications in Body Oncology -- 10.1 General Principles -- 10.2 Brain -- 10.2.1 Glioma -- 10.3 Neck -- 10.3.1 Head and Neck Cancer -- 10.3.2 Thyroid Cancer -- 10.4 Thorax -- 10.4.1 Lung Cancer -- 10.5 Abdomen -- 10.5.1 Esophageal Cancer -- 10.5.2 Liver Tumor -- 10.5.3 Prostate Cancer -- 10.6 Skeleton -- 10.6.1 Bone Metastases -- 10.7 Hematopoietic System -- 10.7.1 Lymphoma -- 10.7.2 Multiple Myeloma -- References -- 11: Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging -- 11.1 Introduction to AI -- 11.2 AI to Improve Image Quality and Processing -- 11.2.1 Image Denoising -- 11.2.2 Image Reconstruction -- 11.2.3 AI Applications in Attenuation Correction -- 11.2.4 Image Segmentation -- 11.2.5 CT Segmentation: Coronary Artery Calcium -- 11.2.6 CT Segmentation: Epicardial Adipose Tissue -- 11.3 AI to Improve Physician Interpretation -- 11.3.1 Structured Reporting -- 11.3.2 Disease Diagnosis -- 11.3.3 Risk Prediction -- 11.4 Protocol Optimization: Application to Rest Scan Cancellation -- 11.5 Explainable AI -- 11.6 Summary -- References -- Part III: Impact of AI and ML on Molecular Imaging and Theranostics -- 12: Artificial Intelligence Will Improve Molecular Imaging, Therapy and Theranostics. Which Are the Biggest Advantages for Therapy? -- 12.1 Introduction -- 12.2 Literature Review -- 12.2.1 Morphological and Metabolic Tumor Volume Tracking -- 12.2.1.1 Volumetry-Based Oncological Response Assessment Frameworks. 327 $a12.2.1.2 Automated Segmentation-Based Volumetry Techniques -- 12.2.1.3 Evolution of Automated Segmentation Using Neural Networks -- 12.3 Quantitative Image and Texture Analysis in Oncological Therapy Response Monitoring -- 12.3.1 Neuro-Oncology -- 12.3.2 Head and Neck Cancers -- 12.3.3 Lung Cancer -- 12.3.4 Prostate Cancer -- 12.3.5 Breast Cancer -- 12.3.6 Gastrointestinal Oncology -- 12.4 Discussion and Outlook -- References -- 13: Integrative Computational Biology, AI, and Radiomics: Building Explainable Models by Integration of Imaging, Omics, and Clinical Data -- 13.1 Introduction -- 13.2 Artificial Intelligence and Data-Driven Science -- 13.3 Multimodal Imaging and Radiomics -- 13.4 Integrative Computational Biology -- 13.5 Patient-Centric Medicine: Preventive and Data-Driven -- References -- 14: Legal and Ethical Aspects of Machine Learning: Who Owns the Data? -- 14.1 Introduction -- 14.2 Opening the "Ethics Bubble": What Are the Concerns? -- 14.3 Going Beyond FAT: Beyond Medical Ethics -- 14.4 Who Owns Patient Data? -- 14.5 Conclusion -- References -- 15: Artificial Intelligence and the Nuclear Medicine Physician: Clever Is as Clever Does -- 15.1 I Am Looking Forward to More A.I. in My Practice Because... -- 15.1.1 The Images Will Look Prettier -- 15.1.2 My Life Will Be Easier -- 15.1.3 My Patients Will Be Better Off -- 15.2 I Am Wary of More A.I. Because... -- 15.2.1 I Don't Understand It -- 15.2.2 I Don't Trust It -- 15.2.3 I Don't Want It -- 15.3 How to Proceed? Let's Be Practical! -- References -- Correction to: Radiomics in Nuclear Medicine, Robustness, Reproducibility, and Standardization -- Correction to: Chapter 3 in: P. Veit-Haibach, K. Herrmann (eds.), Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging, https://doi.org/10.1007/978-3-031-00119-2_3. 330 $aThis book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic. A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice. 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