@inproceedings{c77326a4400d4eb7b0c61ab630ac3715,
title = "Single fiber OCT imager for breast tissue classification based on deep learning",
abstract = "We investigated a deep learning strategy to analyze optical coherence tomography image for accurate tissue characterization based on a single fiber OCT probe. We obtained OCT data from human breast tissue specimens. Using OCT data obtained from adipose breast tissue (normal tissue) and diseased tissue as confirmed in histology, we trained and validated a convolutional neural network (CNN) for accurate breast tissue classification. We demonstrated tumor margin identification based CNN classification of tissue at different spatial locations. We further demonstrated CNN tissue classification in OCT imaging based on a manually scanned single fiber probe. Our results demonstrated that OCT imaging capability integrated into a low-cost, disposable single fiber probe, along with sophisticated deep learning algorithms for tissue classification, allows minimally invasive tissue characterization, and can be used for cancer diagnosis or surgical margin assessment.",
keywords = "Artificial intelligence, Convolutional neural network, Optical coherence tomography, Tissue characterization",
author = "Yuwei Liu and Basil Hubbi and Xuan Liu",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XX 2020 ; Conference date: 01-02-2020 Through 02-02-2020",
year = "2020",
doi = "10.1117/12.2547015",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Israel Gannot and Israel Gannot",
booktitle = "Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XX",
address = "United States",
}