Image-based Heterogeneity Modeling

Image-Based Heterogeneity Modeling is a computational framework that characterizes spatial heterogeneity in imaging data by explicitly modeling local variations in image intensity, texture, or morphology to distinguish underlying biological or pathological states.

26

Publications

1.3K

Citations

138

Authors

74

Institutions

Publications per year

2017–2026

13

Authors

138

  1. VB
    Veerabhadran Baladandayuthapani

    The University of Texas MD Anderson Cancer Center, United States

    2 publications · 76 citations

    2

    h-index

  2. AR
    Arvind Rao

    The University of Texas MD Anderson Cancer Center, United States

    2 publications · 76 citations

    2

    h-index

  3. EW
    Eric Wolsztynski

    University College Cork, Ireland

    1 publications · 20 citations

    1

    h-index

  4. BP
    Benjamin P. Bowen

    Lawrence Berkeley National Laboratory, United States

    1 publications · 19 citations

    1

    h-index

  5. SM
    Shuangge Ma

    Yale University, United States

    1 publications · 18 citations

    1

    h-index

1–5 of 138

Venues

  1. Magnetic Resonance in Medicine

    MRM · 2 publications · 225 citations

    2

    h-index

  2. NeuroImage

    1 publications · 214 citations

    1

    h-index

  3. Journal of Cardiovascular Magnetic Resonance

    1 publications · 12 citations

    1

    h-index

  4. Biometrics

    1 publications · 18 citations

    1

    h-index

  5. BMC Medical Imaging

    1 publications · 30 citations

    1

    h-index

1–5