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    Federated Learning for Medical Imaging

    Udkommer d. 01.03.2025

    Federated Learning for Medical Imaging

    - Principles, Algorithms, and Applications

    Forfatter: info mangler
    Bog
    • Format
    • Bog, paperback
    • Engelsk

    Beskrivelse

    Federated Learning for Medical Imaging: Principles, Algorithms and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. In addition, it provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc. This is a complete resource for computer scientists and engineers as well as clinicians and medical care policymakers wanting to learn about the application of federated learning to medical imaging.

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    Detaljer
    • SprogEngelsk
    • Sidetal260
    • Udgivelsesdato01-03-2025
    • ISBN139780443236419
    • Forlag Academic Press Inc
    • FormatPaperback
    Størrelse og vægt
    coffee cup img
    10 cm
    book img
    19,1 cm
    23,5 cm

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    Medical imaging Algorithms Privacy Fairness Reconstruction OPTIMIZATION Aggregation Data integrity Big data ai Healthcare Personalization Utility Heterogeneous datasets Meta-learning Transfer Learning MRI Data heterogeneity Multitask learning Differential privacy cross-domain Federated learning Data diversity Knowledge Distillation Uncertainty Estimation privacy</p> <p>Federated learning Protecting Patient Privacy model weights federated learning platform postprocessing resilience non-IID data label noise framework features postprocessing theorem integration of uncertainty cross-environment noisy learning cross-task collaborative model training conditional mutual information federated learning algorithm cross-client distributions optimal uncertainty score noisy client FL systems centralized learning privacy-utility tradeoff domain-agnostic collaborative disease classification
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