This study presents a novel approach to bone age assessment (BAA) using a multi-view, multi-task classification model based on the Sauvegrain method. A straightforward solution to automating the Sauvegrain method, which assesses a maturity score for each landmark in the elbow and predicts the bone age, is to train classifiers independently to score each region of interest (RoI), but this approach limits the accessible information to local morphologies and increases computational costs. As a result, this work proposes a self-accumulative vision transformer (SAT) that mitigates anisotropic behavior, which usually occurs in multi-view, multi-task problems and limits the effectiveness of a vision transformer, by applying token replay and regional attention bias. A number of experiments show that SAT successfully exploits the relationships between landmarks and learns global morphological features, resulting in a mean absolute error of BAA that is 0.11 lower than that of the previous work. Additionally, the proposed SAT has four times reduced parameters than an ensemble of individual classifiers of the previous work. Lastly, this work also provides informative implications for clinical practice, improving the accuracy and efficiency of BAA in diagnosing abnormal growth in adolescents.

本研究提出了一种基于视觉变换器的多视角、多任务分类模型的骨龄评估方法，该方法应用了令牌重播和区域注意偏置，可缓解多视角、多任务问题中通常发生的各向异性行为，成功利用了标志物之间的关系并学习了全局形态特征，使得骨龄评估的平均绝对误差比以前的工作低了0.11，并且参数减少了四倍，并且对临床实践提出了对诊断青春期异常生长的准确性和效率的指导意见。

基于Sauvegrain方法的自积累视觉Transformer用于骨龄评估