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

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

J Biomed Opt

Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United StatesfWinship Cancer Institute of Emory University, Atlanta, Georgia, United StatesgEmory University, Department of Radiology and Imaging Sciences, Atlanta, Georgia, United StateshEmory University, Department of Mathematics and Computer Science, Atlanta, Georgia, United States.

Published: June 2017

AI Article Synopsis

  • * A CNN (Convolutional Neural Network) classifier was created to differentiate between squamous-cell carcinoma, thyroid cancer, and normal tissue based on HSI data.
  • * Initial results from 50 patients suggest that this method could enable automatic tissue-labeling in head and neck cancer surgeries, improving diagnostic accuracy.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482930PMC
http://dx.doi.org/10.1117/1.JBO.22.6.060503DOI Listing

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