@inproceedings{742663a2fcf14e0d91cf9d9413d2c1a3,
title = "Deep transfer learning for DOI domain transformation",
abstract = "In the study transfer learning was employed to adapt the previously developed deep networks, 1D_BNCNN and 2D_BNCNN, to handle elliptical phantoms in DOI. The network was fine-tuned using the newly acquired elliptical phantom dataset by leveraging the knowledge and pre-trained weights obtained from the circular phantom dataset. This approach can potentially enhance the realism and accuracy of DOT imaging, enabling more precise characterization of biological tissues and structures.",
keywords = "BNCNN, Diffuse optical imaging, domain transformation, elliptical phantom, flexible opto-measurement channels, ringscanning module, transfer learning",
author = "Nazish Murad and Pan, {Min Chun} and Hsu, {Ya Fen}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences 2024 ; Conference date: 27-01-2024 Through 29-01-2024",
year = "2024",
doi = "10.1117/12.3008599",
language = "???core.languages.en_GB???",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Liang Gao and Guoan Zheng and Lee, {Seung Ah}",
booktitle = "Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences",
}