Attention Eraser and Quantitative Measures for Automated Bone Age Assessment  

作　　者：Liuqiang Shu Lei Yu 

机构地区：[1]College of Computer and Information Science,Chongqing Normal University,Chongqing,401331,China

出　　处：《Computers, Materials & Continua》2025年第1期627-644,共18页计算机、材料和连续体（英文）

基　　金：supported by the grant from the National Natural Science Foundation of China(No.72071019);grant from the Natural Science Foundation of Chongqing(No.cstc2021jcyj-msxmX0185).

摘　　要：Bone age assessment(BAA)aims to determine whether a child’s growth and development are normal concerning their chronological age.To predict bone age more accurately based on radiographs,and for the left-hand X-ray images of different races model can have better adaptability,we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for BAA.In this study,a lightweight feature extractor(LFE)is designed to obtain the featuremaps fromradiographs,and amodule called attention erasermodule(AEM)is proposed to capture the fine-grained features.Meanwhile,the dimensional information of the metacarpal parts in the radiographs is measured to enhance the model’s generalization capability across images fromdifferent races.Ourmodel is trained and validated on the RSNA,RHPE,and digital hand atlas datasets,which include images from various racial groups.The model achieves a mean absolute error(MAE)of 4.42 months on the RSNA dataset and 15.98 months on the RHPE dataset.Compared to ResNet50,InceptionV3,and several state-of-the-art methods,our proposed method shows statistically significant improvements(p<0.05),with a reduction in MAE by 0.2±0.02 years across different racial datasets.Furthermore,t-tests on the features also confirm the statistical significance of our approach(p<0.05).

关 键 词：Bone age assessment attention eraser quantitative feature metacarpal bones 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]