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

• URL: http://arxiv.org/abs/2104.14629v1
• Date: Thu, 29 Apr 2021 19:46:18 GMT
• Title: Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph
• Authors: Xiao-Yun Zhou, Bolin Lai, Weijian Li, Yirui Wang, Kang Zheng, Fakai Wang, Chihung Lin, Le Lu, Lingyun Huang, Mei Han, Guotong Xie, Jing Xiao, Kuo Chang-Fu, Adam Harrison, Shun Miao
• Abstract summary: Based on a fully-supervised graph-based method, DAG, we proposed a semi-supervised extension of it, termed few-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. We extensively evaluated our method on pelvis, hand and chest landmark detection tasks.
• Score: 19.588348005574165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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