Attention Eraser and Quantitative Measures for Automated Bone Age Assessment

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 feature maps from radiographs, and a module called attention eraser module (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 from different races. Our model 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).