This work reports the empirical performance of an automated medical landmark detection method for predict clinical markers in hip radiograph images. Notably, the detection method was trained using a label-only augmentation scheme; our results indicate that this form of augmentation outperforms traditional data augmentation and produces highly sample efficient estimators. We train a generic U-Net-based architecture under a curriculum consisting of two phases: initially relaxing the landmarking task by enlarging the label points to regions, then gradually eroding these label regions back to the base task. We measure the benefits of this approach on six datasets of radiographs with gold-standard expert annotations.

本文通过在髋部X光图像中自动检测医学标志物的实证表现，报道了一种预测临床标记物的自动化医学标志物检测方法。结果显示，该标志物检测方法使用纯标签增强方案进行训练，优于传统的数据增强方法并产生高度样本效率的估计器。我们通过使用基于通用U-Net架构的课程进行训练，逐步放宽通过将标签点扩大为区域来定位标志物的任务，并逐渐侵蚀这些标签区域回到基本任务。我们使用六个具有金标准专家注释的放射图像数据集来评估该方法的效益。

髋骨X射线图像中的医学标志物检测的标签增强方法