Adaptive deep learning for head and neck cancer detection using hyperspectral imaging

Ling Ma, Guolan Lu, Dongsheng Wang, Xulei Qin, Zhuo Georgia Chen, Baowei Fei

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


It can be challenging to detect tumor margins during surgery for complete resection. The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model. Specifically, an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel. According to the output hypothesis of each pixel, the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights. The auto-encoder network is again trained based on these updated pixels. The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels, and thus can improve the detection performance. The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32% and a specificity of 91.31% in our animal experiments. This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection, especially, for the tumor whose margin is indistinct and irregular.

Original languageEnglish (US)
Article number18
JournalVisual Computing for Industry, Biomedicine, and Art
Issue number1
StatePublished - Dec 2019


  • Adaptive learning
  • Deep learning
  • Hyperspectral imaging
  • Noninvasive cancer detection

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software
  • Computer Science (miscellaneous)
  • Medicine (miscellaneous)
  • Visual Arts and Performing Arts


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