Landmark localization plays an important role in medical image analysis.
Learning based methods, including CNN and GCN, have demonstrated the
state-of-the-art performance. However, most of these methods are
fully-supervised and heavily rely on manual labeling of a large training
dataset. In this paper, based on a fully-supervised graph-based method, DAG, we
proposed a semi-supervised extension of it, termed few-shot DAG, \ie five-shot
DAG. It first trains a DAG model on the labeled data and then fine-tunes the
pre-trained model on the unlabeled data with a teacher-student SSL mechanism.
In addition to the semi-supervised loss, we propose another loss using JS
divergence to regulate the consistency of the intermediate feature maps. We
extensively evaluated our method on pelvis, hand and chest landmark detection
tasks. Our experiment results demonstrate consistent and significant
improvements over previous methods.

本文提出一种基于 few-shot DAG 的半监督扩展方法，使用带 SSL 机制的教师 - 学生训练模型，在局部匹配中使用 JS 散度法来规范中间特征图的一致性，该方法在盆骨、手和胸部的地标检测任务中评估，结果较之前方法有显著提升。