Journal
JOURNAL OF BIOMEDICAL OPTICS
Volume 22, Issue 6, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JBO.22.6.060503
Keywords
hyperspectral imaging; convolutional neural network; cancer detection; deep learning; image-guided surgery
Funding
- NIH [CA176684, CA156775, CA204254]
- Georgia Cancer Coalition Distinguished Clinicians and Scientists Award
- Winship Cancer Institute of Emory University [P30CA138292]
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Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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