Bone age assessment using hand-wrist X-ray images is fundamental when diagnosing growth disorders of a child or providing a more patient-specific treatment. However, as clinical procedures are a subjective assessment, the accuracy depends highly on the doctor’s experience. Motivated by this, a deep learning-based computer-aided diagnosis method was proposed for performing bone age assessment. Inspired by clinical approaches and aimed to reduce expensive manual annotations, informative regions localization based on a complete unsupervised learning method was firstly performed and an image-processing pipeline was proposed. Subsequently, an image model with pre-trained weights as a backbone was utilized to enhance the reliability of prediction. The prediction head was implemented by a Multiple Layer Perceptron with one hidden layer. In compliance with clinical studies, gender information was an additional input to the prediction head by embedded into the feature vector calculated from the backbone model. After the experimental comparison study, the best results showed a mean absolute error of 6.2 months on the public RSNA dataset and 5.1 months on the additional dataset using MobileNetV3 as the backbone.

在诊断儿童的生长障碍或提供更针对患者的治疗时，使用腕部 X 射线图像进行骨龄评估是必不可少的。然而，由于临床程序是一种主观评估，其准确性在很大程度上取决于医生的经验。受此启发，提出了一种基于深度学习的计算机辅助诊断方法来进行骨龄评估。受临床方法的启发，旨在减少昂贵的手动注释，首先执行基于完整无监督学习方法的信息区域定位，并提出了图像处理管道。随后，使用具有预训练权重作为主干的图像模型来提高预测的可靠性。预测头由具有一个隐藏层的多层感知器实现。根据临床研究，性别信息通过嵌入到从主干模型计算的特征向量中作为预测头的附加输入。经过实验比较研究，最好的结果显示，公共 RSNA 数据集的平均绝对误差为 6.2 个月，使用 MobileNetV3 作为主干的附加数据集的平均绝对误差为 5.1 个月。