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Landmark localization plays an important role in medical image analysis.Learning based methods, including CNN and GCN, have demonstrated thestate-of-the-art performance. However, most of these methods arefully-supervised and heavily rely on manual labeling of a large trainingdataset. In this paper, based on a fully-supervised graph-based method, DAG, weproposed a semi-supervised extension of it, termed few-shot DAG, \ie five-shotDAG. It first trains a DAG model on the labeled data and then fine-tunes thepre-trained model on the unlabeled data with a teacher-student SSL mechanism.In addition to the semi-supervised loss, we propose another loss using JSdivergence to regulate the consistency of the intermediate feature maps. Weextensively evaluated our method on pelvis, hand and chest landmark detectiontasks. Our experiment results demonstrate consistent and significantimprovements over previous methods.

Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph