Scalable Semi-supervised Landmark Localization for X-ray Images Using Few-Shot Deep Adaptive Graph

Landmark localization plays an important role in medical image analysis. Learning based methods, including convolutional neural network (CNN) and graph convolutional network (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, deep adaptive graph (DAG), we proposed a semi-supervised extension of it, termed few-shot DAG, i.e., 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 semi-supervised learning (SSL) mechanism. In addition to the semi-supervised loss, we propose another loss using Jensen–Shannon (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.