Synopsis
Meta-learning methods have to be properly applied in a wide range of healthcare settings through a huge number of theoretical and experimental studies. Meta-learning in health care remains in its infancy and has as yet not appealed to a larger scientific community, even with the developments. Additional work attempts need to be done to bridge a gap in the literature by combining existing breakthroughs and their practical applications since it is the first systematic survey targeting meta-learning within this area. The scope of the adaptation is where meta-learning also diverges from base learning. Whereas its acquisition is all about acquiring proficiency in a number of learning tasks, base-level acquisition is aimed at acquiring knowledge in one particular learning task. These are data integration across heterogeneous modalities, model interpretability, privacy and security, and real-world scalability optimization. Addressing these areas will enable meta-learning to become a foundation of the next generation of healthcare technologies, providing more accurate, personalized, and efficient diagnostic systems that can ultimately lead to better patient outcomes.
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Integrated Meta- Learning Into Biomedical Diagnostics
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Integrated Meta- Learning Into Biomedical Diagnostics
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Meta-learning methods have to be properly applied in a wide range of healthcare settings through a huge number of theoretical and experimental studies. Meta-learning in health care remains in its infancy and has as yet not appealed to a larger scientific community, even with the developments. Additional work attempts need to be done to bridge a gap in the literature by combining existing breakthroughs and their practical applications since it is the first systematic survey targeting meta-learning within this area. The scope of the adaptation is where meta-learning also diverges from base learning. Whereas its acquisition is all about acquiring proficiency in a number of learning tasks, base-level acquisition is aimed at acquiring knowledge in one particular learning task. These are data integration across heterogeneous modalities, model interpretability, privacy and security, and real-world scalability optimization. Addressing these areas will enable meta-learning to become a foundation of the next generation of healthcare technologies, providing more accurate, personalized, and efficient diagnostic systems that can ultimately lead to better patient outcomes. 80 pp. Englisch. Seller Inventory # 9786208441517
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Integrated Meta- Learning Into Biomedical Diagnostics
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Paperback. Condition: new. Paperback. Meta-learning methods have to be properly applied in a wide range of healthcare settings through a huge number of theoretical and experimental studies. Meta-learning in health care remains in its infancy and has as yet not appealed to a larger scientific community, even with the developments. Additional work attempts need to be done to bridge a gap in the literature by combining existing breakthroughs and their practical applications since it is the first systematic survey targeting meta-learning within this area. The scope of the adaptation is where meta-learning also diverges from base learning. Whereas its acquisition is all about acquiring proficiency in a number of learning tasks, base-level acquisition is aimed at acquiring knowledge in one particular learning task. These are data integration across heterogeneous modalities, model interpretability, privacy and security, and real-world scalability optimization. Addressing these areas will enable meta-learning to become a foundation of the next generation of healthcare technologies, providing more accurate, personalized, and efficient diagnostic systems that can ultimately lead to better patient outcomes. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786208441517
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Integrated Meta- Learning Into Biomedical Diagnostics
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INTEGRATED META- LEARNING INTO BIOMEDICAL DIAGNOSTICS
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Meta-learning methods have to be properly applied in a wide range of healthcare settings through a huge number of theoretical and experimental studies. Meta-learning in health care remains in its infancy and has as yet not appealed to a larger scientific community, even with the developments. Additional work attempts need to be done to bridge a gap in the literature by combining existing breakthroughs and their practical applications since it is the first systematic survey targeting meta-learning within this area. The scope of the adaptation is where meta-learning also diverges from base learning. Whereas its acquisition is all about acquiring proficiency in a number of learning tasks, base-level acquisition is aimed at acquiring knowledge in one particular learning task. These are data integration across heterogeneous modalities, model interpretability, privacy and security, and real-world scalability optimization. Addressing these areas will enable meta-learning to become a foundation of the next generation of healthcare technologies, providing more accurate, personalized, and efficient diagnostic systems that can ultimately lead to better patient outcomes.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. Seller Inventory # 9786208441517
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INTEGRATED META- LEARNING INTO BIOMEDICAL DIAGNOSTICS
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 620844151X
ISBN 13: 9786208441517
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Meta-learning methods have to be properly applied in a wide range of healthcare settings through a huge number of theoretical and experimental studies. Meta-learning in health care remains in its infancy and has as yet not appealed to a larger scientific community, even with the developments. Additional work attempts need to be done to bridge a gap in the literature by combining existing breakthroughs and their practical applications since it is the first systematic survey targeting meta-learning within this area. The scope of the adaptation is where meta-learning also diverges from base learning. Whereas its acquisition is all about acquiring proficiency in a number of learning tasks, base-level acquisition is aimed at acquiring knowledge in one particular learning task. These are data integration across heterogeneous modalities, model interpretability, privacy and security, and real-world scalability optimization. Addressing these areas will enable meta-learning to become a foundation of the next generation of healthcare technologies, providing more accurate, personalized, and efficient diagnostic systems that can ultimately lead to better patient outcomes. Seller Inventory # 9786208441517