ArticleProceedings of SPIE--the International Society for Optical Engineering2020
Single fiber OCT imager for breast tissue classification based on deep learning.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 citations in OpenAlex.
- Prevention of Gastric Cancer.Medicina (Kaunas, Lithuania) · 2026Review
- Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography.Biomedical optics express · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
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