Deep Learning Breast Density Estimation

Deep Learning Breast Density Estimation is a computational methodology that employs deep neural networks to automatically quantify breast tissue density from medical imaging data, distinguishing itself from traditional radiologic and statistical approaches by leveraging hierarchical feature representations learned directly from large datasets.

31

Publications

2.1K

Citations

217

Authors

88

Institutions

Publications per year

2017–2026

30

Authors

217

  1. CM
    Celine M. Vachon

    Mayo Clinic, United States

    2 publications · 508 citations

    2

    h-index

  2. BN
    Brian N. Dontchos

    Massachusetts General Hospital, United States

    2 publications · 330 citations

    2

    h-index

  3. AY
    Adam Yala

    Massachusetts General Hospital, United States

    2 publications · 330 citations

    2

    h-index

  4. CZ
    Chuan Zhou

    University of Michigan, United States

    2 publications · 76 citations

    2

    h-index

  5. JW
    Jun Wei

    University of Michigan, United States

    2 publications · 76 citations

    2

    h-index

1–5 of 217

Venues

  1. Medical Physics

    3 publications · 131 citations

    3

    h-index

  2. Journal of Medical Imaging

    JMI · 2 publications · 110 citations

    2

    h-index

  3. 2

    h-index

  4. Computer Methods and Programs in Biomedicine

    2 publications · 115 citations

    2

    h-index

  5. Journal of Digital Imaging

    2 publications · 79 citations

    2

    h-index

1–5