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

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

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.

Original languageEnglish (US)
Article number060503
JournalJournal of biomedical optics
Volume22
Issue number6
DOIs
StatePublished - Jun 1 2017

Keywords

  • Cancer detection
  • Convolutional neural network
  • Deep learning
  • Hyperspectral imaging
  • Image-guided surgery

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering

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