Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging.
- DOI
- 10.3390/diagnostics16091370
- Published
- 2026 Apr 30
- Container
- Diagnostics (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/diagnostics16091370,
title = {Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging.},
author = {Nam S and Park SH},
year = {2026},
journal = {Diagnostics (Basel, Switzerland)},
doi = {10.3390/diagnostics16091370},
url = {https://doi.org/10.3390/diagnostics16091370}
}RIS
TY - JOUR TI - Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging. AU - Nam S AU - Park SH PY - 2026 JO - Diagnostics (Basel, Switzerland) DO - 10.3390/diagnostics16091370 UR - https://doi.org/10.3390/diagnostics16091370 ER -
APA
S, N., & SH, P. (2026). Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics16091370
Source records
- pubmed · retrieved 2026-09-25T11:10:37.650Z