rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine learning and artificial intelligence in radiation oncology
    sub title: a guide for clinicians
  name:
    name part: Kang, John
    role:
      role term: editor.
  name:
    name part: Rattay, Tim
    role:
      role term: editor.
  name:
    name part: Rosenstein, Barry S.
    role:
      role term: editor.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2024
    copyright date: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations
  abstract: "Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology."--Provided by publisher.
  table of contents: Fundamentals and overview. Fundamentals of machine learning. Abstract -- Artificial intelligence and machine learning -- Capturing and quantifying experience -- Learning from experience -- Nature of automated learning -- Types of machine learning -- Summary -- References -- Artificial intelligence, machine learning, and bioethics in clinical medicine. Abstract -- Introduction -- Principles of bioethics and AI/ML in clinical medicine and research -- Ethical partnerships with AI/ML : recommendations for clinicians -- Conclusion -- References -- Machine learning applications in cancer genomics. Abstract -- Introduction -- Overview of genomic technologies -- Applications of genomics in oncology -- Common hurdles of machine learning in genomics -- Future directions -- References -- Radiomics : "unlocking the potential of medical images for precision radiation oncology." Abstract -- Introduction -- Radiomics workflow -- Potential pitfalls in the radiomics pipeline -- Recommendations for the standardization of radiomics research -- A practical roadmap for radiomics research in radiation oncology -- Translation of radiomics into the clinic -- Conclusion -- References -- Deep learning for medical image segmentation. Abstract -- Clinical need for automated image segmentation -- Rationale of using deep learning for medical image segmentation -- Typical deep learning framework -- Practical considerations for segmentation model learning -- Image pre-processing -- Image patch selection -- Data augmentation -- Model fusion and output uncertainty assessment -- Role of international competitions in medical image segmentation -- AI-based image segmentation and beyond -- Conclusion -- References -- Natural language processing in oncology. Abstract -- What is natural language processing? -- NLP in oncology -- Use cases for NLP in oncology -- The role of the clinician in developing an NLP system -- General NLP tasks and challenges -- Medical NLP tasks and challenges -- NLP approaches --…
  note: edited by John Kang, Tim Rattay, Barry S. Rosenstein.
  note: Includes bibliographical references and index.
  subject:
    topic: Cancer
    topic: Radiotherapy
    topic: Data processing
  subject:
    topic: Artificial intelligence
    topic: Medical applications
  subject:
    topic: Machine learning
  subject:
    topic: Machine Learning
  subject:
    topic: Cancer
    topic: Radiothérapie
    topic: Informatique
  subject:
    topic: Intelligence artificielle
    topic: Applications en médecine
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Artificial intelligence
    topic: Medical applications
  subject:
    topic: Cancer
    topic: Radiotherapy
    topic: Data processing
  subject:
    topic: Machine learning
  classification: RC271.R3
  classification: 616.9940642
  classification: W 26.55.M2
  related item:
    title info:
      title: Machine learning and artificial intelligence in radiation oncology
    origin info:
      publisher: Amsterdam : Academic Press, 2023
    identifier: (OCoLC)1402274751
  identifier: 0128220015
  identifier: 9780128220016
  identifier: 9780128220009
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OCLKB
    record creation date: 231205
    record change date: 20260327175329.4
    record identifier: in00005772689
    language of cataloging:
      language term: eng