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

TL;DR

This paper introduces a semi-supervised, few-shot graph-based method for accurate landmark localization in X-ray images, reducing the need for extensive manual labeling while maintaining high performance.

Contribution

It extends the fully-supervised DAG method to a semi-supervised setting with a teacher-student SSL mechanism and feature consistency loss, enabling effective learning with limited labeled data.

Findings

Significant improvements over previous methods in landmark detection accuracy.

Effective semi-supervised learning with only five labeled examples per class.

Validated on pelvis, hand, and chest X-ray datasets.

Abstract

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.