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[null,"1.","Record Nr.",null,"UNINA9910580165603321"]
[null,null,"Titolo",null,"Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging / / edited by Patrick Veit-Haibach, Ken Herrmann"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"3-031-00119-2"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (216 pages)"]
[null,null,"Collana",null,"Medicine Series"]
[null,null,"Disciplina",null,"610.28563"]
[null,null,"Soggetti",null,"Nuclear medicine"]
[{"$nulls":4},"Medical informatics"]
[{"$nulls":4},"Nuclear Medicine"]
[{"$nulls":4},"Health Informatics"]
[{"$nulls":4},"Intel·ligència artificial en medicina"]
[{"$nulls":4},"Diagnòstic per la imatge"]
[{"$nulls":4},"Medicina nuclear"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,"Nota di contenuto",null,"Intro -- Foreword -- Preface: Benefits and Challenges of AI/ML in Hybrid Imaging and Molecular Imaging -- Contents -- Part I: Technology -- 1: Role and Influence of Artificial Intelligence in Healthcare, Hybrid Imaging, and Molecular Imaging -- 1.1  AI Applications Support the Infrastructure and Interventions of Healthcare, Including Molecular Imaging -- 1.1.1  Drug Development -- 1.1.2  Clinical Workflow -- 1.2  AI's Clinical Applications with a Focus on Molecular Imaging -- 1.2.1  Understanding Disease -- 1.2.2  Diagnosis -- 1.2.3  Radiologic-Pathology Correlation -- 1.2.4  Characterization -- 1.2.5  Treatment Planning -- 1.2.6  Prediction of Response to Treatment -- 1.2.7  Overall Prognosis -- 1.2.8  Reporting -- 1.3  Conclusion -- References -- 2: Introduction to Machine Learning: Definitions and Hybrid Imaging Applications -- 2.1  Introduction -- 2.2  History and Basic Definitions -- 2.3  Learning Paradigms -- 2.4  General Concepts of Machine Learning Methods -- 2.5  Classical Machine Learning Approaches -- 2.6  Artificial Neural Networks -- 2.7  Radiomics and Radiogenomics -- 2.8  Imaging Applications -- 2.9  Conclusions and Perspectives -- References -- 3: "]
[{"$nulls":4},"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."]
[{"$nulls":4},"5.1.1  The Task of ML -- 5.1.1.1  A Question -- 5.1.1.2  A Computer -- 5.1.1.3  An Algorithm or Model -- 5.1.1.4  Data to Interpret -- 5.1.2  Supervised Learning -- 5.1.3  Unsupervised Learning -- 5.1.4  Radiomics and Texture Analysis -- 5.1.5  Feature Reduction -- 5.1.6  Scaling and Normalization -- 5.1.7  Training, Validation, and Testing -- 5.2  Linear Regression -- 5.2.1  Under- and Overfitting -- 5.2.2  Linear Regression Mathematics -- 5.2.3  The Neural Network -- 5.2.4  The Objective Function -- 5.2.5  Gradient Descent -- 5.2.6  Deep Learning with Convolutional Neural Networks -- 5.2.7  Advanced Deep Learning Architectures -- 5.2.7.1  Autoencoders -- 5.2.7.2  ResNet -- 5.2.7.3  U-Net -- 5.2.7.4  Generative Adversarial Networks -- 5.2.7.5  Deep Boltzmann Machines -- 5.2.8  Deep Learning in Medical Image Analysis -- 5.2.8.1  Classification, Localization and Detection -- 5.2.8.2  Segmentation -- 5.2.8.3  Registration -- 5.2.8.4  Image Synthesis -- 5.2.9  Federated Learning -- References -- Part II: Clinical Applications -- 6: Imaging Biomarkers and Their Meaning for Molecular Imaging -- 6.1  Introduction -- 6.2  Imaging Biomarkers, Paradigm Shift in Medical Imaging -- 6.3  Imaging Biomarkers in Hybrid Molecular Imaging -- References -- 7: Integration of Artificial Intelligence, Machine Learning, and Deep Learning into Clinically Routine Molecular Imaging -- 7.1  Introduction -- 7.2  Classification -- 7.3  Segmentation -- 7.4  Detection and Localization -- 7.5  Applications of ML and DL in Molecular Imaging -- 7.6  Internal Department Applications -- 7.7  A Glance at Tomorrow -- 7.8  Workforce -- Redundancy, Displacement, Transformation, and Opportunity -- 7.9  Summary -- References -- 8: Imaging Biobanks for Molecular Imaging: How to Integrate ML/AI into Our Databases -- 8.1  Introduction -- 8.2  Imaging Biobanks in Molecular Imaging."]
[{"$nulls":4},"8.3  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 "]
[{"$nulls":4},"-- 11.3  AI to Improve Physician Interpretation -- 11.3.1  Structured Reporting -- 11.3.2  Disease Diagnosis -- 11.3.3  Risk Prediction -- 11.4  Protocol Optimization: Application to Rest Scan Cancellation -- 11.5  Explainable AI -- 11.6  Summary -- References -- Part III: Impact of AI and ML on Molecular Imaging and Theranostics -- 12: Artificial Intelligence Will Improve Molecular Imaging, Therapy and Theranostics. Which Are the Biggest Advantages for Therapy? -- 12.1  Introduction -- 12.2  Literature Review -- 12.2.1  Morphological and Metabolic Tumor Volume Tracking -- 12.2.1.1  Volumetry-Based Oncological Response Assessment Frameworks."]
[{"$nulls":4},"12.2.1.2  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."]
[null,null,null,"Sommario/riassunto","This 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. As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists, Physicists, Medical Imaging Administrators and Nuclear Medicine Technologists alike."]