Deep Learning-based Tracking

Deep Learning-Based Tracking is a cell tracking methodology that employs deep neural networks to learn spatiotemporal representations from image sequences, distinguishing itself from traditional tracking approaches by leveraging hierarchical feature abstraction and end-to-end learning for accurate and automated cell lineage estimation.

726

Publications

67.6K

Citations

2.5K

Authors

729

Institutions

Publications per year

2017–2026

660

Authors

2.5K

  1. MY
    Ming–Hsuan Yang

    University of California, Merced, United States

    19 publications · 4.8K citations

    16

    h-index

  2. CM
    Chao Ma

    Shanghai Jiao Tong University, China

    16 publications · 4K citations

    15

    h-index

  3. HL
    Huchuan Lu

    Dalian University of Technology, China

    12 publications · 2.6K citations

    12

    h-index

  4. FS
    Fahad Shahbaz Khan

    Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates

    9 publications · 4.5K citations

    9

    h-index

  5. BZ
    Bineng Zhong

    Huaqiao University, China

    9 publications · 1.5K citations

    9

    h-index

1–5 of 2.5K

Venues

  1. arXiv (Cornell University)

    Repository · 78 publications · 3.7K citations

    29

    h-index

  2. IEEE Transactions on Image Processing

    26 publications · 2.7K citations

    23

    h-index

  3. 17

    h-index

  4. IEEE Access

    28 publications · 953 citations

    17

    h-index

  5. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 12 publications · 1.7K citations

    12

    h-index

1–5