Data-centric physics-inspired deep learning framework for saturation artifact removal in optical coherence tomography
- DOI
- 10.1016/j.media.2026.104211
- Published
- 2026-09
- Container
- Medical Image Analysis
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.media.2026.104211,
title = {Data-centric physics-inspired deep learning framework for saturation artifact removal in optical coherence tomography},
author = {Jonas Nienhaus and Thomas Schlegl and Florian Kapeller and Ryan Sentosa and Katharina Dettelbacher and Philipp Matten and Hessam Roodaki and Wolfgang Drexler and Tilman Schmoll and Rainer A. Leitgeb},
year = {2026},
journal = {Medical Image Analysis},
doi = {10.1016/j.media.2026.104211},
url = {https://doi.org/10.1016/j.media.2026.104211}
}RIS
TY - JOUR TI - Data-centric physics-inspired deep learning framework for saturation artifact removal in optical coherence tomography AU - Jonas Nienhaus AU - Thomas Schlegl AU - Florian Kapeller AU - Ryan Sentosa AU - Katharina Dettelbacher AU - Philipp Matten AU - Hessam Roodaki AU - Wolfgang Drexler AU - Tilman Schmoll AU - Rainer A. Leitgeb PY - 2026 JO - Medical Image Analysis DO - 10.1016/j.media.2026.104211 UR - https://doi.org/10.1016/j.media.2026.104211 ER -
APA
Nienhaus, J., Schlegl, T., Kapeller, F., Sentosa, R., Dettelbacher, K., Matten, P., Roodaki, H., Drexler, W., Schmoll, T., & Leitgeb, R. A. (2026). Data-centric physics-inspired deep learning framework for saturation artifact removal in optical coherence tomography. Medical Image Analysis. https://doi.org/10.1016/j.media.2026.104211
Source records
- crossref · retrieved 2026-09-26T03:30:09.314Z