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  • Book
    Issam El Naqa, Martin J. Murphy, editors.
    Summary: This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities. .

    Contents:
    Part I. Introduction
    1. What are Machine and Deep Learning?
    2. Computational Learning Basics
    3. Overview of Conventional Machine Learning Methods
    4. Overview of Deep Machine Learning Methods
    5. Quantum Computing for Machine Learning
    6. Performance Evaluation
    7. Software Tools for Machine and Deep learning
    8. Data sharing, protection and bioethics
    Part II. Machine Learning for Medical Image Analysis
    9. Detection of Cancer Lesions from Imaging
    10. Diagnosis of Malignant and Benign Tumours
    11. Auto-contouring for image-guidance and treatment planning
    Part III. Machine Learning for Treatment planning & Delivery
    12. Quality Assurance and error prediction
    13. Knowledge-based treatment planning
    14. Intelligent respiratory motion management
    Part IV. Machine Learning for Outcomes Modeling and Decision Support
    15. Prediction of oncology treatment outcomes
    16. Radiomics and radiogenomics
    17. Modelling of Radiotherapy Response (TCP/NTCP)
    18. Smart adaptive treatment strategies
    19. Machine learning in clinical trials.
    Digital Access Springer 2022
  • Article
    Fujiwara K, Porter ME, Pollard TD.
    J Cell Biol. 1978 Oct;79(1):268-75.
    We used antibodies against alpha-actinin and myosin labeled directly with contrasting fluorochromes to localize these contractile proteins simultaneously in dividing chick embryo cells. During mitosis anti-alpha-actinin stains diffusely the entire cytoplasm including the mitotic spindle, while in the same cells intense antimyosin staining delineates the spindle. During cytokinesis both antibodies stain the cleavage furrow intensely, and until the midbody forms the two staining patterns in the same cell are identical at the resolution of the light microscope. Thereafter the anti-alpha-actinin staining of the furrow remains strong, but the antimyosin staining diminishes. These observations suggest that alpha-actinin participates along with actin and myosin in the membrane movements associated with cytokinesis.
    Digital Access Access Options