Deep transfer learning for DOI domain transformation

Nazish Murad; Min-Chun Pan; Ya-Fen Hsu

doi:10.1117/12.3008599

13 March 2024 Deep transfer learning for DOI domain transformation

Nazish Murad, Min-Chun Pan, Ya-Fen Hsu

Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 128570O (2024) https://doi.org/10.1117/12.3008599
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nazish Murad, Min-Chun Pan, and Ya-Fen Hsu "Deep transfer learning for DOI domain transformation", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570O (13 March 2024); https://doi.org/10.1117/12.3008599

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