Transfer learning improves performance in volumetric electron microscopy organelle segmentation across tissues
Our rat liver dataset's raw image volume, manual ground truth annotation, and model predictions are freely shared at github.com/Xrioen/cross-tissue-transfer-learning-in-VEM.
Authors
- Gökhan S. Hotamışlıgil (ORCID: https://orcid.org/0000-0003-2906-1897)
- Gary D. Bader (ORCID: https://orcid.org/0000-0003-0185-8861)
- Ali Izadi‐Darbandi (ORCID: https://orcid.org/0000-0001-6107-6453)
- Feysal M. Ali (ORCID: https://orcid.org/0000-0001-8477-3438)
- Ronald Xie
- Sagar Marwah
- Ben Mulcahy (ORCID: https://orcid.org/0000-0002-3336-245X)
- Mei Zhen (ORCID: https://orcid.org/0000-0003-0086-9622)
- Sonya A. MacParland (ORCID: https://orcid.org/0000-0002-8036-1425)
- Güneş Parlakgül (ORCID: https://orcid.org/0000-0001-9365-7287)
- Yuna Lee (ORCID: https://orcid.org/0000-0002-4339-9301)
- Wang Bo (ORCID: https://orcid.org/0000-0001-6652-0525)
Institutions
- Broad Institute (US)
- University Health Network (CA)
- Harvard University (US)
- University of Toronto (CA)
- Lunenfeld-Tanenbaum Research Institute (CA)
- Princess Margaret Cancer Centre (CA)
- Vector Institute (CA)
- Cellular Research (United States) (US)
- University of California, Berkeley (US)
Publication Details
- Journal
- Bioinformatics Advances
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/bioadv/vbaf021
- Citations
- 2
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.32