GFDet:Multi-Level Feature Fusion Network for Caries Detection Using Dental Endoscope Images  

作　　者：Nan Gao Yukai Li Peng Chen Jijun Tang Tianshuang Liu 

机构地区：[1]College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310000,China [2]Department of Computer Science and Engineering,University of South Carolina,Columbia,SC 29208,USA [3]Future Science and Technology City Branch,Hangzhou Stomatology Hospital,Hangzhou 310000,China

出　　处：《Big Data Mining and Analytics》2024年第4期1362-1374,共13页大数据挖掘与分析(英文)

基　　金：supported by the Zhejiang Provincial Natural Science Foundation of China(No.LGF22F020014);the National Key Research and Development Program of China(No.2020YFB1707700);the National Natural Science Foundation of China(Nos.62036009 and U1909203).

摘　　要：Early dental caries detection by endoscope can prevent complications,such as pulpitis and apical infection.However,automatically identifying dental caries remains challenging due to the uncertainty in size,contrast,low saliency,and high interclass similarity of dental caries.To address these problems,we propose the Global Feature Detector(GFDet)that integrates the proposed Feature Selection Pyramid Network(FSPN)and Adaptive Assignment-Balanced Mechanism(AABM).Specifically,FSPN performs upsampling with the semantic information of adjacent feature layers to mitigate the semantic information loss due to sharp channel reduction and enhance discriminative features by aggregating fine-grained details and high-level semantics.In addition,a new label assignment mechanism is proposed that enables the model to select more high-quality samples as positive samples,which can address the problem of easily ignored small objects.Meanwhile,we have built an endoscopic dataset for caries detection,consisting of 1318 images labeled by five dentists.For experiments on the collected dataset,the F1-score of our model is 75.6%,which out-performances the state-of-the-art models by 7.1%.

关 键 词：caries detection feature fusion label assignment object feature enhancement channel attention 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]