Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging

Martin Halicek; Guolan Lu; James V. Little; Xu Wang; Mihir Patel; Christopher C. Griffith; Mark W. El-Deiry; Amy Y. Chen; Baowei Fei

doi:10.1117/1.JBO.22.6.060503

24 June 2017 Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging

Martin Halicek, Guolan Lu, James V. Little, Xu Wang, Mihir Patel, Christopher C. Griffith, Mark W. El-Deiry, Amy Y. Chen, Baowei Fei

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Journal of Biomedical Optics, Vol. 22, Issue 6, 060503 (June 2017). https://doi.org/10.1117/1.JBO.22.6.060503

Abstract

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.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 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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Martin Halicek, Guolan Lu, James V. Little, Xu Wang, Mihir Patel, Christopher C. Griffith, Mark W. El-Deiry, Amy Y. Chen, and Baowei Fei "Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging," Journal of Biomedical Optics 22(6), 060503 (24 June 2017). https://doi.org/10.1117/1.JBO.22.6.060503

Received: 9 May 2017; Accepted: 9 June 2017; Published: 24 June 2017

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