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Position Weighted Convolutional Neural Network for Unbalanced Children Caries Diagnosis

文献类型：期刊论文

作者Zhou, Xiaojie5; Feng, Xueou4; Li, Qingming4; Yin, Qiyue4; Yang, Jun3; Yu, Guoxia2,5; Shi, Qing1
刊名IEEE ACCESS
出版日期2023
卷号11页码:77034-77044
关键词Caries diagnosis CNN transformer position embedding panoramic radiograph
ISSN号2169-3536
DOI10.1109/ACCESS.2023.3294617
通讯作者Yu, Guoxia(yuguoxia@bch.com.cn)
英文摘要Panoramic radiograph is one of the most widely used inspection tools for dentists making caries diagnosis, especially for teeth that are hard to be diagnosed through visual inspection. Recently, several deep learning methods, e.g., based on convolutional neural network (CNN) or transformer network, have been proposed for automatic caries diagnosis on dental panoramic radiographs, and promising results have been achieved. However, current approaches use all the teeth equally when training their models, which results in performance degeneration because of unbalanced classification difficulties for different tooth positions. The objective of this study is to introduce a position weighted CNN to alleviate the above problem for more accurate caries diagnosis. The position weighted module evaluates and revises the output of a specially designed CNN to incorporate position information. In addition, a novel data augmentation method is used to balance data with uneven decayed and normal teeth, which is one of the reasons leading to unbalanced classification difficulty. To verify the proposed method, a children panoramic radiograph database is collected and labeled with more than 6,000 teeth. The proposed approach outperforms the state-of-the-art caries diagnosis methods with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8859, 0.8875, 0.8932, 0.8903 and 0.9315, respectively. Specially, the proposed model displays higher diagnosis performance compared with two attending doctors with more than five-year clinical experience but with different diagnosis patterns, showing a potential tool for assisting dentists.
资助项目Respiratory Research Project of National Clinical Research Center for Respiratory Diseases[HXZX-20210402] ; National Natural Science Foundation of China[81800925]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:001041936300001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Respiratory Research Project of National Clinical Research Center for Respiratory Diseases ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/53828]  
专题复杂系统认知与决策实验室
通讯作者Yu, Guoxia
作者单位1.Capital Med Univ, Beijing Stomatol Hosp, Beijing 100050, Peoples R China
2.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Stomatol,Natl Clin Res Ctr Resp Dis, Beijing 100045, Peoples R China
3.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
5.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Stomatol, Beijing 100045, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Xiaojie,Feng, Xueou,Li, Qingming,et al. Position Weighted Convolutional Neural Network for Unbalanced Children Caries Diagnosis[J]. IEEE ACCESS,2023,11:77034-77044.
APA Zhou, Xiaojie.,Feng, Xueou.,Li, Qingming.,Yin, Qiyue.,Yang, Jun.,...&Shi, Qing.(2023).Position Weighted Convolutional Neural Network for Unbalanced Children Caries Diagnosis.IEEE ACCESS,11,77034-77044.
MLA Zhou, Xiaojie,et al."Position Weighted Convolutional Neural Network for Unbalanced Children Caries Diagnosis".IEEE ACCESS 11(2023):77034-77044.

入库方式： OAI收割

来源：自动化研究所

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