Evaluating JPEG2000 compression effects on prediction accuracy and storage efficiency in lung cancer whole-slide images: a deep learning-based analysis.
- DOI
- 10.1186/s13000-026-01830-7
- Published
- 2026 Aug 11
- Container
- Diagnostic pathology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1186/s13000-026-01830-7,
title = {Evaluating JPEG2000 compression effects on prediction accuracy and storage efficiency in lung cancer whole-slide images: a deep learning-based analysis.},
author = {Ji H and Yook M and Jung CK and Choi IY},
year = {2026},
journal = {Diagnostic pathology},
doi = {10.1186/s13000-026-01830-7},
url = {https://doi.org/10.1186/s13000-026-01830-7}
}RIS
TY - JOUR TI - Evaluating JPEG2000 compression effects on prediction accuracy and storage efficiency in lung cancer whole-slide images: a deep learning-based analysis. AU - Ji H AU - Yook M AU - Jung CK AU - Choi IY PY - 2026 JO - Diagnostic pathology DO - 10.1186/s13000-026-01830-7 UR - https://doi.org/10.1186/s13000-026-01830-7 ER -
APA
H, J., M, Y., CK, J., & IY, C. (2026). Evaluating JPEG2000 compression effects on prediction accuracy and storage efficiency in lung cancer whole-slide images: a deep learning-based analysis.. Diagnostic pathology. https://doi.org/10.1186/s13000-026-01830-7
Source records
- pubmed · retrieved 2026-11-10T13:58:09.430Z