Machine Learning-based Planning

Machine Learning-Based Planning is a computational framework within cancer treatment planning that employs machine learning algorithms to model and optimize therapeutic interventions by learning patterns from clinical and biological data, thereby distinguishing it from conventional rule-based or manual planning approaches.

78

Publications

3.7K

Citations

456

Authors

173

Institutions

Publications per year

2017–2026

62

Authors

456

  1. FY
    F Yin

    Duke Medical Center, United States

    3 publications · 384 citations

    4

    h-index

  2. QW
    Qiuwen Wu

    Duke Medical Center, United States

    3 publications · 463 citations

    4

    h-index

  3. CM
    Colin M. Carpenter

    Stanford University, United States

    4 publications · 155 citations

    4

    h-index

  4. GV
    Gilmer Valdés

    University of California, San Francisco, United States

    4 publications · 337 citations

    4

    h-index

  5. TD
    Timothy D. Solberg

    University of California, Los Angeles, United States

    3 publications · 235 citations

    3

    h-index

1–5 of 456

Venues

  1. 5

    h-index

  2. Physica Medica

    4 publications · 164 citations

    4

    h-index

  3. Physics and Imaging in Radiation Oncology

    PIRO · 3 publications · 147 citations

    3

    h-index

  4. Radiation Oncology

    3 publications · 67 citations

    3

    h-index

  5. Advances in Radiation Oncology

    2 publications · 38 citations

    3

    h-index

1–5