Semi-supervised Segmentation

Semi-Supervised Segmentation is a machine learning approach within image segmentation that leverages a small set of labeled images and a larger set of unlabeled images to train a model, distinguished from fully supervised and unsupervised methods by its joint use of labeled and unlabeled data to improve segmentation accuracy under limited annotation.

663

Publications

41.8K

Citations

2.5K

Authors

740

Institutions

Publications per year

2017–2026

554

Authors

2.5K

  1. YW
    Yunchao Wei

    Beijing Jiaotong University, China

    9 publications · 1K citations

    9

    h-index

  2. PH
    Pheng‐Ann Heng

    Chinese University of Hong Kong, Hong Kong SAR China

    9 publications · 1K citations

    9

    h-index

  3. CD
    Christian Desrosiers

    École de Technologie Supérieure, Canada

    9 publications · 554 citations

    9

    h-index

  4. MP
    Marco Pedersoli

    École de Technologie Supérieure, Canada

    8 publications · 430 citations

    8

    h-index

  5. LS
    Ling Shao

    Inception Institute of Artificial Intelligence, United Arab Emirates

    7 publications · 2K citations

    7

    h-index

1–5 of 2.5K

Venues

  1. IEEE Transactions on Medical Imaging

    34 publications · 3.5K citations

    28

    h-index

  2. arXiv (Cornell University)

    Repository · 80 publications · 3.3K citations

    28

    h-index

  3. Medical Image Analysis

    22 publications · 2.8K citations

    21

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 18 publications · 1.3K citations

    17

    h-index

  5. 14

    h-index

1–5