Adaptive deep learning for head and neck cancer detection using hyperspectral imaging
Abstract 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. Spec...
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doaj-eb6f61dd41744a0c954dbf1c20bc65c62020-11-25T04:12:07ZengSpringerOpenVisual Computing for Industry, Biomedicine, and Art2524-44422019-11-012111210.1186/s42492-019-0023-8Adaptive deep learning for head and neck cancer detection using hyperspectral imagingLing Ma0Guolan Lu1Dongsheng Wang2Xulei Qin3Zhuo Georgia Chen4Baowei Fei5Department of Radiology and Imaging Sciences, Emory UniversityDepartment of Radiology and Imaging Sciences, Emory UniversityDepartment of Hematology and Medical Oncology, Emory UniversityDepartment of Radiology and Imaging Sciences, Emory UniversityDepartment of Hematology and Medical Oncology, Emory UniversityDepartment of Radiology and Imaging Sciences, Emory UniversityAbstract 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.http://link.springer.com/article/10.1186/s42492-019-0023-8Hyperspectral imagingDeep learningAdaptive learningNoninvasive cancer detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ling Ma Guolan Lu Dongsheng Wang Xulei Qin Zhuo Georgia Chen Baowei Fei |
spellingShingle |
Ling Ma Guolan Lu Dongsheng Wang Xulei Qin Zhuo Georgia Chen Baowei Fei Adaptive deep learning for head and neck cancer detection using hyperspectral imaging Visual Computing for Industry, Biomedicine, and Art Hyperspectral imaging Deep learning Adaptive learning Noninvasive cancer detection |
author_facet |
Ling Ma Guolan Lu Dongsheng Wang Xulei Qin Zhuo Georgia Chen Baowei Fei |
author_sort |
Ling Ma |
title |
Adaptive deep learning for head and neck cancer detection using hyperspectral imaging |
title_short |
Adaptive deep learning for head and neck cancer detection using hyperspectral imaging |
title_full |
Adaptive deep learning for head and neck cancer detection using hyperspectral imaging |
title_fullStr |
Adaptive deep learning for head and neck cancer detection using hyperspectral imaging |
title_full_unstemmed |
Adaptive deep learning for head and neck cancer detection using hyperspectral imaging |
title_sort |
adaptive deep learning for head and neck cancer detection using hyperspectral imaging |
publisher |
SpringerOpen |
series |
Visual Computing for Industry, Biomedicine, and Art |
issn |
2524-4442 |
publishDate |
2019-11-01 |
description |
Abstract 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. |
topic |
Hyperspectral imaging Deep learning Adaptive learning Noninvasive cancer detection |
url |
http://link.springer.com/article/10.1186/s42492-019-0023-8 |
work_keys_str_mv |
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