Machine Learning For Adaptation

Machine Learning for Adaptation is a computational framework within adaptive radiotherapy that employs machine learning algorithms to dynamically modify treatment plans in response to anatomical or physiological changes by learning from patient-specific longitudinal data.

13

Publications

370

Citations

48

Authors

40

Institutions

Publications per year

2017–2026

6

Authors

48

  1. IV
    Ivan Vujaklija

    Medical University of Vienna, Austria

    1 publications · 21 citations

    1

    h-index

  2. SE
    Saba Ebrahimi

    University of Houston, United States

    1 publications · 34 citations

    1

    h-index

  3. AC
    Antonio Coronato

    Institute for High Performance Computing and Networking, Italy

    1 publications · 18 citations

    1

    h-index

  4. GG
    Girjesh Govil

    Tata Institute of Fundamental Research, India

    1 publications · 17 citations

    1

    h-index

  5. CH
    Cheng‐Geng Huang

    University of Electronic Science and Technology of China, China

    1 publications · 26 citations

    1

    h-index

1–5 of 48

Venues

  1. Practical Radiation Oncology

    1 publications · 26 citations

    1

    h-index

  2. Physica Medica

    1 publications · 36 citations

    1

    h-index

  3. Mechanical Systems and Signal Processing

    MSSP · 1 publications · 26 citations

    1

    h-index

  4. Medical Engineering & Physics

    1 publications · 10 citations

    1

    h-index

  5. Computer Methods and Programs in Biomedicine

    1 publications · 72 citations

    1

    h-index

1–5