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

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,...

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Bibliographic Details
Published in:Visual computing for industry, biomedicine and art biomedicine and art, 2019-11, Vol.2 (1), p.18-12, Article 18
Main Authors: Ma, Ling, Lu, Guolan, Wang, Dongsheng, Qin, Xulei, Chen, Zhuo Georgia, Fei, Baowei
Format: Article
Language:English
Subjects:
Adaptive learning
CAE) and Design
Cancer
Coders
Computer Graphics
Computer Imaging
Computer Science
Computer-Aided Engineering (CAD
Deep learning
Hyperspectral imaging
Image detection
Image Processing and Computer Vision
Machine learning
Media Design
Medical Imaging Modeling
Noninvasive cancer detection
Original
Original Article
Pattern Recognition and Graphics
Pixels
Teaching methods
Tumors
Vision
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Summary: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.
ISSN:2524-4442
2096-496X
2524-4442
DOI:10.1186/s42492-019-0023-8