@inproceedings{7aef83822ecb4839ada153b8f76c7171,
title = "Dual-encoder deep learning networks to enhance diffuse optical imaging for highly scattered nonhomogeneous media",
abstract = "This paper aims to demonstrate a novel deep-learning network that addresses the prediction of breast tumors for diffuse optical imaging. Two learning schemes, signal encoder and image encoder, in the proposed network are designed for reconstructing optical-property images. The former processing method takes boundary data directly to deep networks, and predicts the optical-coefficient distribution, while the latter feeds images obtained by inverse image reconstruction with artifacts and sometimes hard-to-localized tumors. All 10,000 samples of synthesized homogeneous and heterogeneous phantoms were randomly selected for training, validation, and testing of performance. Twelve phantom samples were employed to justify its effectiveness in real applications.",
keywords = "Diffuse optical imaging, dual-encoder deep learning network, measured phantom justification, signal/image encoder",
author = "Nazish Murad and Hsu, {Ya Fen} and Pan, {Min Chun}",
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 = "10.1117/12.3008598",
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",
}