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

作　　者：Ling Ma Guolan Lu Dongsheng Wang Xulei Qin Zhuo Georgia Chen Baowei Fei 

机构地区：[1]Department of Radiology and Imaging Sciences,Emory University,Atlanta,GA 30322,USA [2]College of Software,Nankai University,Tianjin 300350,People’s Republic of China [3]Department of Hematology and Medical Oncology,Emory University,Atlanta,GA 30322,USA [4]Department of Bioengineering,The University of Texas at Dallas,Richardson,TX 75080,USA [5]Department of Radiology,The University of Texas Southwestern Medical Center,Dallas,TX 75390,USA

出　　处：《Visual Computing for Industry,Biomedicine,and Art》2019年第1期164-175,共12页工医艺的可视计算（英文）

基　　金：This work was supported in part by NIH grants(R01CA204254,R01HL140325,and R21CA231911).

摘　　要：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.

关 键 词：Hyperspectral imaging Deep learning Adaptive learning Noninvasive cancer detection 

分 类 号：R73[医药卫生—肿瘤]