Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

Abstract Background Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we ai...

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Main Authors: Chaofeng Li, Bingzhong Jing, Liangru Ke, Bin Li, Weixiong Xia, Caisheng He, Chaonan Qian, Chong Zhao, Haiqiang Mai, Mingyuan Chen, Kajia Cao, Haoyuan Mo, Ling Guo, Qiuyan Chen, Linquan Tang, Wenze Qiu, Yahui Yu, Hu Liang, Xinjun Huang, Guoying Liu, Wangzhong Li, Lin Wang, Rui Sun, Xiong Zou, Shanshan Guo, Peiyu Huang, Donghua Luo, Fang Qiu, Yishan Wu, Yijun Hua, Kuiyuan Liu, Shuhui Lv, Jingjing Miao, Yanqun Xiang, Ying Sun, Xiang Guo, Xing Lv
Format: Article
Language:English
Published: Wiley 2018-09-01
Series:Cancer Communications
Subjects:
Nasopharyngeal malignancy
Deep learning
Differential diagnosis
Automatic segmentation
Online Access:http://link.springer.com/article/10.1186/s40880-018-0325-9

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http://link.springer.com/article/10.1186/s40880-018-0325-9

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