Self-supervised Phenotype Learning

Self-Supervised Phenotype Learning is a computational framework within multimodal phenotyping that leverages unlabeled multimodal data to automatically discover and learn phenotypic representations by exploiting intrinsic data structures and cross-modal consistencies, without relying on predefined labels.

11

Publications

627

Citations

93

Authors

40

Institutions

Publications per year

2017–2026

11

Authors

93

  1. SR
    Siddhi Ramesh

    University of Chicago, United States

    2 publications · 92 citations

    2

    h-index

  2. AT
    Alexander T. Pearson

    University of Chicago, United States

    2 publications · 92 citations

    2

    h-index

  3. CM
    Chiara Maria Lavinia Loeffler

    Heidelberg University, Germany

    2 publications · 92 citations

    2

    h-index

  4. OL
    Oliver Lester Saldanha

    Heidelberg University, Germany

    2 publications · 92 citations

    2

    h-index

  5. JN
    Jan Niehues

    University of Zurich, Switzerland

    2 publications · 92 citations

    2

    h-index

1–5 of 93

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 59 citations

    2

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 2 publications · 36 citations

    2

    h-index

  3. IEEE Transactions on Medical Imaging

    1 publications · 41 citations

    1

    h-index

  4. Nature Machine Intelligence

    1 publications · 268 citations

    1

    h-index

  5. Nature Communications

    1 publications · 59 citations

    1

    h-index

1–5